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Answer all questions in complete English sentences and use proper APA citation when appropriate

1. What about policy implications? 8.5/12.5

2. great, 12.5/12.5

3. You are on the right track. The canonical null hypothesis states that there is no relationship (cross sectional or trend) between the independent and dependent variable. Is there a trend? Or more helpful: under the null hypothesis would there be a trend relationship between heroin initiation and opioid usage over time? 4.5/12.5

4. What about the 150 programs? What about the participants? Is it a probability sample or not? 4.5 /12.5

5.Discuss structured questionnaires more,. What about the Key Informants? What about the structured questionnaire format? 2.5/12.5

6. Give the equation, 2.5/12.5

7. Discuss more by providing some statistics, 9.5/12.5

8. Please review Cicero et al. (2017), 1/12.5

Paper2 (Cicero et al., 2018)

Cicero, T. J. et al. (2018). Increased use of Heroin as an Initiating Opioid of Abuse: Further

Considerations and Policy Implications. Additive Behaviors, 87, 267-271.

Instructions: Answer all questions in complete sentences and use proper APA citation

when appropriate. Each question is worth 11.1 points. Question/Answer format is

required.

PURPOSE OF RESEARCH AND THE ‘TREATMENT GAP’

1. State the purpose of the Cicero et al. (2018) research. How is the 2018 paper an elaboration of the 2017 paper?

The research conducted by (CIcero, Kasper, & Ellis, 2018) is a follow-up a previously by the same scholars on heroin for first-time opioid abusers. In the follow-up research, the scholars aim to explain further the impact of heroin being the gateway option for opioid addicts. In the 2018 study, policy implications instituted to curb opioids abuse are tested to determine the impact they have on the overall opioid pandemic. In the study (Cicero, Ellis, & Kasper, 2017), the methodology employed to obtain data involved qualitative research. A sample size of five thousand, eight hundred and eighty-eight respondents were interviewed about the specific type of opioid product they regularly used and made them sink into addiction. All of the respondents were former users who had approached a rehabilitation institution for substance abuse treatment. In the following year, 2018, the same scholars aim to obtain more intensive data by selecting a sample size of people seeking substance abuse help. Still, their first experience with opioids must be within the last five years (2013-2017).

2. What is the ‘treatment gap’ that Cicero et al. (2018) mentions? How did the ‘treatment gap’ manifest itself in Figure 1 (Cicero et al., 2018, p. 268)?

The treatment gap is the period from when an individual begins abusing a certain until the time when they acknowledge to be having a problem and seek help. On considering the line characteristic curve for heroin as the opioid initiator to the number of individuals coming out to seek help from substance abuse, heroin use as the initiator has grown steadily but constantly. From the year 2005, the percentage of individuals seeking help admitted to having been introduced to opioids through heroin rose from 8.7% to 31.6%. As reflected from the results, people who get introduced into opioid regular use from an initial prescription from oxycodone, hydrocodone, and other opioid family medications take longer before asking for help. As the rate of heroin initiation gets larger, the time from first use to rehabilitation is significantly reduced.

(Cicero, Kasper, & Ellis, 2018, p. 268)

To adjust for the treatment gap, the scholars (CIcero, Kasper, & Ellis, 2018), since the accuracy of the results could be greatly affected if the respondents’ sample is not large enough, the researchers examine the number of patients who are recruited into the substance abuse recovery center from the SKIP program. SKIP stands for, nationwide Survey of Key Informants’ Patients program. It is an essential player for the RADARS system. RADARS system stands for; Researched Abuse, Diversion, and Addiction-Related Surveillance protocol. It is a set of programs that obtain and process information on the abuse of opioids through street heroin opioid-based pain reliever prescriptions.

HYPOTHESES

3. If the main research hypothesis, in stylized form, for this trend study, is

The annual percentage of SKIP respondents who indicated initiating regular opioid use with heroin exhibited a steadily increasing trend relative to the yearly percentage of those SKIP respondents who indicated initiating regular opioid use with prescription opioids over the 2005-2015 time period.

State the corresponding null hypothesis.

(HINT: dependent variable is “opioid of first regular use” with levels – heroin versus prescription opioids (hydrocodone and/or oxycodone) – and independent variable is time or more specifically, “year beginning regular use of opioid.” Figure 1 may be helpful.)

The relative increase in Heroin availability compared to hydrocodone/oxycodone prescription is not why it’s the opioid of choice for first-time regular use, from 2013 to 2017.

SAMPLING AND SURVEY

4. As best you can, describe the sample design for the SKIP Program.

SKIP’s sample design is a distributed sampling that utilizes one hundred and fifty public and private substance abuse recovery centers.

5. What is the data-collection mode of administration used by SKIP Program? What sort of

interview format was employed in SKIP Program survey? (HINT: cf, Dixon et al., 2019,

pps. 215-216 and pps. 221- 229).

The questionnaire design involves bounded questions. The interview type adopted for the study is the semi-structured type.

FINDING AND LIMITATIONS

6. Discuss the construction of variable named year beginning regular opioid use and how

Cicero et al. (2018) used it in the analysis of the SKIP Program data.

The variable named ‘year beginning opioid use’ was a variable since the opioid-based pain killer prescription policies directly impact hydrocodone and oxycodone.

7. What are the study’s main findings?

Unlike the study conducted in 2017, the follow-up research reflects the impact of installed policies towards opioids abuse. While controlling opioid prescription has reduced hydrocodone and oxycodone abuse, it has created a niche for heroin on opioid-dependent individuals. In the data obtained from the recovering individuals, people who had admitted to having at least abused heroin or opioid prescribed medication were also interviewed to observe the correlation and comparison of illegal and legal drug abuse and the treatment gap. While the study conducted in 2017 aims to determine the percentage of individuals introduced into opioids through heroin, the follow-up 2018 research focuses on establishing a correlation between the initiation opioid (heroin or prescription) and the treatment gap. Although the treatment gap for people who get initiated through heroin is considerably smaller, it is the most fatal due to overdose risk since street drugs don’t feature a piece of quantifiable, empirical potency information. For instance, heroin can be mixed with compounds such as fentanyl that pose more danger to overdose.

8. What are the limitations of the study? (HINT: Do not forget to consider the nature of the sample.)

The national data obtained from the RADARS’ SKIP platform is owned by Denver Health which has all exclusive rights for the information. Users who have not participated in the data collection or analysis cannot access the unprocessed data. Such a setting restricts pure data acquisition even for the subscribers. Since all the scholars didn’t have direct access to the unprocessed information, the research was based on already processed information.

References

Dixon, J. C., Singleton, R. A., & Straits, B. (2019). The Process of Social Research, (2nd ed.). NY: OUP.

Cicero, T. J. et al. (2017). Increased use of Heroin as an Initiating Opioid of Abuse. Additive Behaviors, 74, 63-67.

Contents lists available at ScienceDirect

Addictive Behaviors

journal homepage: www.elsevier.com/locate/addictbeh

Short Communication

Increased use of heroin as an initiating opioid of abuse

Theodore J. Cicero⁎, Matthew S. Ellis, Zachary A. Kasper
Washington University in St. Louis, Department of Psychiatry, Campus Box 8134,

0 S. Euclid Avenue, St. Louis, MO 63110, United States

H I G H L I G H T S

• Heroin as the first opioid of abuse has grown significantly in the past decade.
• Heroin as an initiating opioid now exceeds hydrocodone and oxycodone.
• Such increases among inexperienced opioid users could lead to increased risk of overdose.

A R T I C L E I N F O

Keywords:
Heroin
Heroin overdose
Prescription opioid abuse
Opioid abuse
Opioid initiation

A B S T R A C T

Introduction: Given the relatively recent growth in access to heroin and a more permissive atmosphere sur-
rounding its use, we hypothesized that an increasing number of persons with limited experience and tolerance to
opioids would experiment with heroin as their first opioid rather than more common prescription opioid an-
algesics.
Methods: Individuals entering substance abuse treatment for an opioid use disorder in the period 2010–2016
(N = 5885) were asked about the specific opioid they first regularly used to get high. To limit long-term recall
and survival bias, analyses was restricted to opioid initiation that occurred in the past ten years (2005–2015).
Results: In 2005, only 8.7% of opioid initiators started with heroin, but this sharply increased to 33.3%
(p < 0.001) in 2015, with no evidence of stabilization. The use of commonly prescribed opioids, oxycodone and hydrocodone, dropped from 42.4% and 42.3% of opioid initiators, respectively, to 24.1% and 27.8% in 2015, such that heroin as an initiating opioid was now more frequently endorsed than prescription opioid analgesics. Conclusions: Our data document that, as the most commonly prescribed opioids – hydrocodone and oxycodone – became less accessible due to supply-side interventions, the use of heroin as an initiating opioid has grown at an alarming rate. Given that opioid novices have limited tolerance to opioids, a slight imprecision in dosing in- herent in heroin use is likely to be an important factor contributing to the growth in heroin-related over dose fatalities in recent years.

1. Introduction

The United States has been burdened by an epidemic of opioid
abuse and overdose deaths over the past two decades (Manchikanti,
Fellows, Ailinani, & Pampati, 2010). It began in earnest in the 1990s
with sudden increases in the number of prescribed opioids, in particular
novel extended release opioids not adulterated with acetaminophen or
NSAIDs (e.g., OxyContin®) (U.S. OxyContin abuse and diversion and
efforts to address the problem [Report to Congressional Requesters,
#GAO-04-110], 2003). As the diversion of prescription opioids in-
creased, they were perceived by abusers to be safer due to their legality
and readily apparent brand and dose specificity, which helped avoid
accidental overdose (Daniulaityte, Falck, & Carlson, 2012). In an effort
to address this growing problem, a number of “supply” reduction efforts

were launched by federal agencies and the pharmaceutical companies
benefitting from the sale of these products. These include, but are not
limited to, statewide prescription monitoring programs intended to
discourage doctor shopping and “script doctors” (Brady et al., 2014),
increased physician education on the appropriate use of opioids
(Alford, 2016), and the development of abuse deterrent formulations of
opioids which make it difficult to crush or solubilize tablets for insuf-
flation or IV injection (Cicero & Ellis, 2015a).

Recent studies have shown that these supply-reduction efforts have
been modestly successful in reducing the supply of prescribed opioids
and subsequent diversion for non-therapeutic purposes (Dart et al.,
2015a). Thus, those already dependent on prescription opioids were
faced with a dilemma: find more money to buy harder to find and more
expensive prescription opioids, or find a cheaper alternative. For many,

http://dx.doi.org/10.1016/j.addbeh.2017.05.030
Received 20 March 2017; Received in revised form 18 May 2017; Accepted 22 May 2017

⁎ Corresponding author.
E-mail address: Cicerot@wustl.edu (T.J. Cicero).

Addictive Behaviors 74 (2017) 63–66

Available online 23 May 2017
0306-4603/ © 2017 Published by Elsevier Ltd.

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http://dx.doi.org/10.1016/j.addbeh.2017.05.030

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the solution was a transition to heroin, a popular alternative given its
steadily lower price, making it more widely accessible and with a high
comparable, if not stronger, than prescription opioids (Cicero, Ellis,
Surratt, & Kurtz, 2014; Compton, Jones, & Baldwin, 2016). Thus, to
meet increased demand, there has been a significant dealer-driven in-
crease in supply. As a result of this increased supply of cheap, accessible
heroin, we hypothesized a spillover effect, where an increasing number
of persons inexperienced with opioids might begin to experiment with
readily available heroin as their first opioid of abuse, rather than a less
risky, but also less accessible prescription opioid. Given the imprecision
in titrating doses and the potential for potent adulterants (e.g., fentanyl
analogues), we anticipated that, should our hypothesis be supported,
this emerging trend could very well be associated with an increase in
heroin related overdose fatalities, particularly in novice opioid users
who lack the degree of tolerance found in more experienced ones.

To determine whether there has been, in fact, an increase in the use
of heroin as a first opioid, we analyzed data on opioid use initiation
patterns (i.e., first opioid regularly used) using self-administered sur-
veys in opioid-dependent patients (N = 5885) entering one of over 150
substance abuse treatment programs around the country from 2011 to
2016.

2. Methods

This report utilized data from the ongoing nationwide Survey of Key
Informants’ Patients (SKIP) Program, a key element of the Researched
Abuse, Diversion and Addiction-Related Surveillance (RADARS®)
System, a comprehensive series of programs that collect and analyze
post-marketing data on the abuse and diversion of prescription opioid
analgesics and heroin (Cicero et al., 2007; Dart et al., 2015b). The SKIP
Program consists of a Key Informant network with annual participation
of over 150 public and privately funded treatment centers, with a
reasonable representativeness of the four census areas (Region [SKIP %,
2014 Census%]; Midwest [27.9%,21.2%], Northeast [15.5%,17.6%],
South [33.6%,37.6%] and West [23.0%,23.6%]). Key Informants were
asked to recruit clients (eighteen years and older) who were entering
their substance abuse treatment program with a primary diagnosis of
opioid use disorder, as defined by DSM-IV or V criteria, depending on
the time of completion. Clients were asked to complete an anonymous
paper survey centered on opioid abuse patterns and related behaviors,
with an 85% response rate attained. The survey packet included a $20
Wal-Mart gift card and a self-addressed stamped envelope which, after
completion, was used by the respondent to mail the survey (identified
by a unique case number) directly to Washington University in St. Louis
(WUSTL). All protocols were approved by the WUSTL Institutional
Review Board.

In addition to demographics, SKIP respondents, analyzed from 2011
to 2016, were asked the specific opioid they first regularly used (i.e.
2+ times a month), categorized as ‘prescription opioid’ or ‘heroin’, and
the age they began to regularly use opioids. The year regular use began
was calculated (Year of survey completion − Age at survey completion
+ Age of first regular opioid use = Year of beginning regular opioid use),
with the analyses restricted to those initiating use within the past ten
years (2005–2015; N = 5885) to limit long-term recall and survival
bias (no respondent who completed a survey in 2016 initiated opioid
use in that same year), a time-period shown to have stable recall for
opioid abuse (Shillington, Cottler, Mager, & Compton, 1995). Cochran-
Armitage test for trend was used to determine significant changes in
first opioids over time and differences in demographic characteristics
between heroin and prescription opioid initiates were assessed using
Pearson Chi-Square tests of comparison using IBM SPSS Statistics v24.

3. Results

3.1. First opioid

Fig. 1 shows the annual unadjusted proportion of total SKIP re-
spondents who indicated initiating regular opioid use with prescription
opioids (hydrocodone, oxycodone or other prescription opioids) or
heroin from 2005 to 2015. Only 8.7% of opioid initiates who began
regular use in 2005 started with heroin, but its use sharply increased
thereafter to the point where in 2015, heroin as an initiating opioid was
at its highest point, 33.3% (p < 0.001), with no evidence of stabili- zation. Hydrocodone and oxycodone were the most widely identified initiating opioids over the ten year period. While they were equally attractive for first time users in 2005 (42.4% and 42.3%, respectively), at levels in excess of heroin for several years (~10%), their rate of endorsements began to gradually decrease as initiation with heroin increased. By 2015, hydrocodone and oxycodone were at 24.1% (p < 0.001) and 27.8% (p = 0.13), respectively, below the 33.3% proportion of heroin – now the leading drug for new opioid initiates.

3.2. Demographic comparisons of recent heroin and prescription opioid
initiates

As shown in Table 1, heroin initiates were compared to prescription
opioid initiates across a number of demographic variables. While pre-
scription opioid initiates were slightly older, had higher rates of college
education, were slightly more likely to be white and tended to reside in
more non-urban areas, all of these differences were very small (yet
significant, given the sample size). Interestingly, there were no sig-
nificant sex differences between the two groups.

4. Discussion

The rapid four-fold increase in the use of heroin by new initiates to
opioid use from 2005 to 2015 is a striking finding with significant
public health implications. Given the imprecision in estimating a cor-
rect dose of heroin, largely due to uncertainty about its purity and
potential adulterants (i.e., fentanyl analogues), the possibility of over-
dose in opioid novices is considerable and seemingly inevitable given
that they lack the ability to tolerate even small errors in calculating
their initial dose of heroin. Obviously, we could not ascertain directly
whether this was true in the users surveyed (i.e., with overdose death as
the endpoint), but the possibility of overdose and death is a clear and
present danger which must be addressed, particularly by agencies

Fig. 1. First opioid of regular use among opioid initiates from 2005 to 2015 (N = 5885).
Cochran-Armitage trend tests showed significant changes for heroin (< .0.001), hydro- codone (< 0.001), other prescription opioids (< 0.001), but not oxycodone (p = 0.13).

T.J. Cicero et al. Addictive Behaviors 74 (2017) 63–66

charged with protecting the public health so that, as a country, we are
much better prepared to deal with this aspect of the opioid problem
than we were in recognizing the initial emergence of prescription
opioid abuse in the 1990s and the recent transition to heroin.

While we lack the ability to determine the plausibility of our pos-
tulate that those who initiate opioid use with heroin are more likely to
overdose or die, it is noteworthy that the Centers for Disease Control
has reported that the national overdose deaths attributable to heroin
rose from 2009 deaths in 2005 to 12,989 in 2015 (National Institute on
Drug Abuse, 2017; Rudd et al., 2016), the same time period analyzed in
this study. While there are no data in the CDC databases to link those
deaths with prior drug histories, the two variables – heroin as the first
opioid of use and fatal overdose – seem to be highly correlated (See
Supplemental Fig. 1, Spearman correlation rs = 0.81). Obviously, ad-
ditional, more directed studies will be needed to establish causality, but
these data are certainly suggestive.

The fact that we did not find major differences in the demographics
of those initiating opioid use with prescription opioids or heroin in-
dicates that the two groups are not clearly distinguishable, which
makes interventions and prevention more difficult. What is probably
equally, or more, important is that heroin and/or prescription opioids
are being interchangeably used in roughly the same population
(Cicero & Ellis, 2015b). In this connection, it should be noted, that, in
the long-term, there has been a significant shift in the demographic of
heroin users over the last fifty years. In the 1960s–1970s, heroin use
was primarily an inner-city, minority issue, whereas today, the “epi-
demic” has shifted to include a larger proportion of white, suburban-
rural residents (Cicero et al., 2014).

It is clear from a number of studies that supply reduction efforts –
e.g., prescription monitoring programs and abuse deterrent drug for-
mulations – have been effective in reducing the supply of prescription
opioids on the black market (Dart et al., 2015a). However, it also seems
clear that the reduction in the supply of prescription opioids has had
both intended and unintended consequences. The number of opioids
prescribed by physicians has declined in recent years (Pezalla et al.,
2017) and, if one assumes that this reflects better awareness by phy-
sicians of the appropriate use of opioids, and a reduction in the “pill
mills” and doctor shopping, these programs have met positive ex-
pectations. There is, however, one potential unanticipated effect –
physicians may be more reluctant to prescribe opioids to treat pain
patients due to widely publicized negative aspects of these drugs.

The clear message from the foregoing discussion is that supply re-
duction efforts alone will not completely solve our current epidemic of
heroin use and overdose deaths. History has taught us that as long as
there is a demand, dealers and clever, motivated users will find a way to
acquire their preferred types of drugs or introduce newly manufactured
ones (e.g., illicit fentanyl analogues) that are not subject to the supply
side limitations currently in place (Boettke, Coyne, & Hall, 2013). Ob-
viously, we are not arguing against targeted supply reduction efforts –
they clearly can be helpful – but we need to stress how incomplete they
are unless we begin to address demand reduction efforts as well.

There are important limitations to our studies. Most significantly,
ours is a retrospective study. While there are potential issues of recall in
such studies, it would be nearly impossible to practically carry out a
prospective study linking first opioid use with heroin overdose deaths
as a dependent variable. In addition, the time lag between onset of use
and treatment entry resulted in a decrease in Ns available for recent
years, which could impact the generalizability of our findings. Beyond
these practical limitations, it needs to be stressed that our results po-
tentially underestimate the extent of harm to those who initiate regular
opioid use with heroin – so called survival bias (i.e., those who over-
dosed early into their heroin use would obviously not be included in
these studies). Neither we nor the CDC database can compensate for the
inherent study limitations and suggest, albeit indirectly, that the ad-
verse consequences of early heroin adoption are worse than our data
suggest.

Additional limitations include the fact that, since ours is a treat-
ment-based sample, one could also argue that the results are not re-
presentative of those who use opioids “recreationally.” Furthermore,
differences in the factors influencing the decision to enter treatment,
such as family/court pressures and financial ability, could limit the
heterogeneity of our sample. These differences could also vary over the
analyzed period, as well as between those initiating heroin versus
prescription opioids. Future studies should examine, at a deeper level,
the potential impact of these confounders on treatment-seeking beha-
vior.

Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.addbeh.2017.05.030.

Role of funding source

The national data were collected from a subset of participants from
the Survey of Key Informants’ Patients (SKIP) Program, a component of
the RADARS® (Researched Misuse, Diversion and Addiction-Related
Surveillance) System. The RADARS System is supported by subscrip-
tions from pharmaceutical manufacturers for surveillance, research and
reporting services. RADARS System is the property of Denver Health
and Hospital Authority, a political subdivision of the State of Colorado.
Denver Health retains exclusive ownership of all data, databases and
systems. Subscribers do not participate in data collection or analysis,
nor do they have access to the raw data. Dr. Cicero serves as a paid
consultant on the Scientific Advisory Board of the RADARS® System.
None of the authors have a direct financial, commercial or other re-
lationship with any of the subscribers of the RADARS® System.

Contributors

The corresponding author oversaw the development, implementa-
tion and management of the studies involved and takes responsibility
for the integrity of the data and the accuracy of the data analysis, which
was conducted by Ellis and Kasper, in conjunction with the corre-
sponding author. Authors Cicero and Ellis developed and wrote the
manuscript. All authors have reviewed and approved the manuscript.

Conflict of interest

Author Cicero serves as a consultant on the Scientific Advisory

Table 1
Characteristics of heroin vs. prescription opioid initiates, 2005–2015.

Initiate Cohort, No. (%)

Heroin
(n = 631)

Prescription opioid
(n = 5254)

Sig.a

Age at survey completion
(SE)

27.0 (0.28) 28.9 (0.11)

< 0.001b

Gender 0.82
Male 299 (47.8%) 2519 (48.3%)
Female 327 (52.2%) 2701 (51.7%)

Ethnicity 0.01
White 479 (78.0%) 4262 (82.2%)
Non-white 135 (22.0%) 922 (17.8%)

Urbanicity of residence 0.01
Urban 280 (51.6%) 2095 (46.1%)
Suburban/rural 263 (48.4%) 2454 (53.9%)

Highest completed
education

< 0.001b

Some college or more 204 (32.7%) 2141 (41.0%)
Education lower than
college

409 (

.5%) 2994 (57.3%)

None 11 (1.8%) 90 (1.7%)

a Independent samples t-test used for age, Pearson’s chi-squared was used for all other
variables.

b Significant at a level of p ≤ 0.01.

T.J. Cicero et al. Addictive Behaviors 74 (2017) 63–66
65

http://dx.doi.org//10.1016/j.addbeh.2017.05.030

Board of the non-profit post-marketing surveillance system, RADARS®.
Authors Ellis and Kasper have no conflicts of interest to report.

Acknowledgements

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Contents lists available at ScienceDirect

Addictive Behaviors

journal homepage: www.elsevier.com/locate/addictbeh

Short Communication

Increased use of heroin as an initiating opioid of abuse: Further
considerations and policy implications

heodore J. Cicero⁎, Zachary A. Kasper, Matthew S. Ellis
Washington University in St. Louis, Department of Psychiatry, Campus Box 8134, 660 S. Euclid Avenue, St. Louis, MO 63110, United States

H I G H L I G H T S

• Heroin as the first opioid of abuse has grown significantly in the past decade.
• Past month heroin use continues to grow as prescription opioid abuse declines.
• Opioid policies need to be more inclusive of heroin, rather than prescription opioid-specific.

A B S T R A C T

Introduction: Previously, we reported a marked increase in the use of heroin as an initiating opioid in non-
tolerant, first time opioid users. In the current paper, we sought to update and expand upon these results, with a
discussion of the policy implications on the overall opioid epidemic.
Methods: Opioid initiation data from the original study were updated to include surveys completed through
2017 (N = 8382) from a national sample of treatment-seeking opioid users. In addition, past month abuse of
heroin and prescription were analyzed as raw numbers of treatment program entrant in the last five years
(2013–2017), drawing from only those treatment centers that participated every year in that time frame.
Results: The updated data confirm and extend the results of our original study: the use of heroin as an initiating
opioid increased from 8.7% in 2005 to 31.6% in 2015, with increases in overall Ns per initiation year reflecting a
narrowing of the “treatment gap”, the time lag between opioid initiation from 2005 to 2015 and later treatment
admission (up to 2017). Slight decreases were observed in treatment admissions, but this decline was totally
confined to prescription opioid use, with heroin use continuing to increase in absolute numbers.
Conclusions: Given that opioid novices have limited tolerance, the risk of fatal overdose for heroin initiates is
elevated compared to prescription opioids, particularly given non-oral administration and often unknown
purity/adulterants (i.e., fentanyl). Imprecision of titrating dose among opioid novices may explain observed
increases opioid overdoses. Future policy decisions should note that prescription opioid-specific interventions
may have little impact on a growing heroin epidemic.

1. Summary of previous article

In a recent article in this journal, Increased use of heroin as an in-
itiating opioid of abuse (Cicero, Ellis, & Kasper, 2017), we concluded
that there was a significant increase in the number of treatment-seeking
opioid users whose first experience with an opioid was with heroin,
rather than the more recently commonplace pattern of initiating opioid
use with prescription drugs. These results suggest that novice, non-
tolerant opioid users may have a much higher risk of overdose death
due to inexperience in the titration of dose, particularly if fentanyl
analogues are involved, and that these may be contributing to

continued increases in heroin-related overdose fatalities and emergency
room visits (O’Donnell, Gladden, & Seth, 2017; Rudd, Seth, David, &
Increases in Drug, 2016; Seth, Scholl, Rudd, & Bacon, 2018). However,
there were some ambiguities in our data given the relatively low
numbers of individuals represented in more recent years for which we
had data. Thus, we felt the need to update our data after the original
article went to press in order to validate our original findings and un-
derscore the importance of the ‘treatment gap’ in interpreting our data.
That is, there is a time lag between initial opioid exposure and treat-
ment admission, which accounted for the lower overall numbers in
more recent years. In addition, the brevity of our article did not allow

https://doi.org/10.1016/j.addbeh.2018.05.03

Received 7 May 2018; Received in revised form 29 May 2018; Accepted 30 May 2018

⁎ Corresponding author at: Department of Psychiatry, Washington University in St. Louis, School of Medicine, Box 8134, 660 S. Euclid Ave., St. Louis, MO 63110, United States.
E-mail address: cicerot@wustl.edu (T.J. Cicero).

Addictive Behaviors 87 (2018) 267–

271

Available online 31 May 2018
0306-4603/ © 2018 Published by Elsevier Ltd.

http://www.sciencedirect.com/science/journal/03064603

https://www.elsevier.com/locate/addictbeh

https://doi.org/10.1016/j.addbeh.2018.05.030

mailto:cicerot@wustl.edu

https://doi.org/10.1016/j.addbeh.2018.05.030

http://crossmark.crossref.org/dialog/?doi=10.1016/j.addbeh.2018.05.030&domain=pdf

us to expand on two areas of concern. First, our article did not fully
assess policy implications of our data and the extent of concern they
might appropriately generate on the public health burden of the opioid
epidemic. Second, we also did not sufficiently discuss how recent in-
terventions to reduce the supply of prescription opioids may have im-
pacted the opioid epidemic as a whole, in particular its influence on this
new pathway to heroin initiation, which bypasses the more commonly
studied progression from prescription opioids to heroin. As such, we felt
the need to more clearly remove some ambiguity in our data and, most
importantly, to more intensely discuss the policy implications of our
work in this clarification article. To do so, we have included an updated
report on our previously published data, as well as a supplemental
analysis of opioid treatment admission data and the implications of
these data for policy development.

2. Methods for original, updated and supplemental data

While the specifics of our data analysis can be reviewed in the
original article, briefly, our data are sourced from the ongoing nation-
wide Survey of Key Informants’ Patients (SKIP) Program, a key element
of the Researched Abuse, Diversion and Addiction-Related Surveillance
(RADARS®) System, a comprehensive series of programs that collect
and analyze post-marketing data on the abuse and diversion of pre-
scription opioid analgesics and heroin (Cicero et al., 2007; Dart et al.,
2015a). The SKIP Program consists of a Key Informant network with
annual participation of approximately 150 public and privately funded
treatment centers which recruit adult clients entering their substance
abuse treatment program with a primary diagnosis of opioid use dis-
order to complete an anonymous paper survey. Initially, SKIP re-
spondents, analyzed from 2011 to 2016, were asked the specific opioid
they first regularly used (i.e. 2+ times a month), categorized as ‘hy-
drocodone’, ‘oxycodone’, ‘heroin’, or ‘other prescription opioids’ [bu-
prenorphine, fentanyl, hydromorphone, methadone, morphine, oxy-
morphone, tapentadol and tramadol], and the age they began to
regularly use opioids. The year regular use began was calculated (

Year

of survey completion – Age at survey completion + Age of first regular
opioid use = Year of beginning regular opioid use), with the analyses
restricted to those initiating use within the past ten years (2005–2015;
N = 5885) to limit long-term recall and survival bias (no respondent
who completed a survey in 2016 initiated opioid use in that same year),
a time-period shown to have stable recall for opioid abuse (Shillington,

Cottler, Mager, & Compton, 1995). These data have been updated for
this report using the same criteria and analysis period, with the inclu-
sion of data from surveys received through the end of 2017.

We also conducted an analysis of the absolute numbers of in-
dividuals who entered and were recruited from each treatment program
per year, restricting our analysis to only those sites that recruited in
every year of the analysis period (N = 66) to analyze a stable sample of
treatment providers. These sites were located in 33 states and saw a
mean number of 53.3 new opioid patients per quarter (range: 5–400),
with a breakdown of 54.5% private, 30.3% public and 15.2% both
private and public. Data are presented as the total number of partici-
pants entering treatment and recruited by the site, with a breakdown of
the number of whom endorsed past month abuse of heroin and/or a
prescription opioid from the past five years (2013–2017), with the data
not mutually exclusive.

3. Results

3.1. Heroin as an initiating opioid

Fig. 1 shows trendlines from the original published figure, re-
presented as dotted lines, and updated trendlines (solid line). The lower
numbers on the x axis represent the original N at each time point
whereas the upper numbers shows the increase in Ns after the inclusion
of data through 2017 (N = 8382). As can be seen, there was a con-
siderable increase in numbers as data has accumulated, but the trends
remained the same. Heroin use as a first opioid grew sharply from 8.7%
of the sample in 2005 to almost 31.6% in 2015. It should be noted that
the numbers in more recent years increased due to the addition of new
data bridging the treatment gap. That is, there is a time gap between
initial opioid exposure which occurred from 2005 to 2015 and treat-
ment admission from 2011 to 2017, which accounts for the lower
overall numbers in more recent years. But, as this figure demonstrates,
as data continue to be collected, these overall numbers will continue to
increase as the treatment gap narrows.

3.2. Opioid treatment admissions

To adjust for the treatment gap (i.e. decrease in the Ns over time),
we expressed data in Fig. 1 as the proportion of the total who used a
prescription opioid or heroin as their initial opioid. This mode of data

Fig. 1. First opioid of regular abuse among opioid initiates from 2005 to 2015 (N = 8382).

T.J. Cicero et al. Addictive Behaviors 87 (2018) 267–271

268

presentation, while a correct representation of the data, could be sus-
pect if the number of people entering treatment selecting either a
prescription opioid or heroin declined, particularly if prescription
opioids declined at a faster rate than heroin. To test the validity of this
hypothetical scenario we examined the raw number of patients entering
the treatment clinics participating in the SKIP program in the last five
years who indicated past month abuse of heroin and/or a prescription
opioid. These data are shown in Fig. 2. The total number of those en-
tering our stable sample of treatment centers decreased modestly over
time (from 1598 to 1421) implying a decrease in overall opioid use if
viewed as a whole. However, this overall decrease was solely attribu-
table to decreases in past month prescription opioid abuse (from 1453
to 1197) The numbers of those endorsing past month abuse of heroin
continually increased over the past five years (from 723 to 980). While
participants could endorse both prescription opioids and heroin, the
data is presented separately in order to depict the total number of users
for each drug category.

4. Discussion

4.1. Overview of results

Our data support prior research which indicate that supply reduc-
tion efforts centered on prescription opioids appear to be having an
effect in reducing abuse of prescription opioids (Dart et al., 2015b). The
most dramatic reductions we observed was for oxycodone and hydro-
codone, with modest, variable increases in other prescription opioids.
While the foregoing data represent the positive news in a sense, there
are two issues highlighted by these data. First, as our opioid initiation
data continue to be updated with increasing Ns, the trend of increases
in heroin initiation remain prevalent and of grave concern. Second,
while the raw numbers of treatment admissions for opioid use disorder
show slight decreases, this looks to be attributable solely to decreases in
past month prescription opioid abuse. Specifically, prescription opioid
abuse decreased, but heroin use increased in absolute numbers. While
prescription opioid abuse still outweighs heroin use at the moment,
these data suggest supply side interventions focused on prescription
opioids alone may have little impact on heroin use. In fact, the decrease

in overall opioid treatment admissions seen in our data may be tem-
porary, with the real possibility that the overall number of opioid
treatment admissions will go back up over time as heroin use continues
to increase and outweighs prescription opioid abuse.

4.2. Importance of increases in heroin use

Prior research has demonstrated the progression to heroin from
those initiating with prescription opioids, often as a result of practical
issues (e.g., cost), reformulations of preferred opioids, or due to re-
ductions in the ability to obtain prescription opioids (Unick,
Rosenblum, Mars & Ciccarone, 2013; Cicero & Ellis, 2015; Compton,
Jones, & Baldwin, 2016; Harocopos & Allen, 2015). However, heroin
markets have apparently markedly ramped up production and dis-
tribution of this drug into the United States, seeking to capitalize on the
demand for opioid drugs (Opsina, Tinajero, & Jelsma, 2018;
Rosenblum, Unick, & Ciccarone, 2014). Coupled with reports of a re-
duced stigma in the use of heroin among previously risk-averse popu-
lations (Cicero, Ellis, Surratt, & Kurtz, 2014), it is perhaps not surprising
that widely available and cheap heroin would supplant prescription
opioids as an initial opioid of abuse. While it is hard to say one – pre-
scription opioid or heroin – is better/worse than the other, it cannot be
denied that new initiates to opioid use through heroin are at increased
risk of overdose than those with prior experience to prescription opioid
due to a number of factors: 1) as opposed to prescription pills with
marked dosage, heroin typically has a purity unknown to the user; 2

)

additives such as the far cheaper fentanyl and its dangerous analogues
(e.g., carfentanyl) may be mixed with the heroin; 3) estimating the dose
is difficult for even experienced users; and 4) an opioid naive individual
who has not yet become tolerant to opioids may be at risk of overdose
with even a singular exposure due a combination of one or more of
these factors. The risk of overdose is thus markedly higher in these new
users than it would be in maximally tolerant, long-term users. This may
account for the rapidly escalating number of overdose deaths experi-
enced in recent years (Rudd et al., 2016; Seth et al., 2018). While most
overdose data lacks information relevant to the characteristics of opioid
exposure, it is difficult not to conclude that at least some of this increase
is due to inexperienced, first time users underestimating their dose. Of

723
829

889 874
980

1453 1443
1341

1256
1197

200

400

600

800

1000

1200

1400

1600

2013 2014 2015 2016 2017

N
um

be
r o

f t
re

at
m

en
t a

dm
is

si
on

s
(n

)
Year

Past Month Heroin Past Month Prescrip�on Opioid

n=1598 n=1629

n=1558

n=1502
n=1421

Fig. 2. Number of treatment-seeking opioid users recruited from a stable sample of 66 Key Informant sites from 2013 to 2017, with the number of those endorsing
past month heroin use and/or past month abuse of at least one prescription opioid, with data not mutually exclusive.

T.J. Cicero et al. Addictive Behaviors 87 (2018) 267–271

269

course, the growing rise in the use of fentanyl analogues as an addition
to street purchased heroin has certainly increased the risk of overdose
in all users, but it is hard to disagree that heroin use in opioid naive
individuals isn’t a definitive risk factor contributing to this increase in
opioid overdose deaths.

4.3. Policy implications

Our data indicate that while supply reduction efforts targeting
prescription opioid abuse may have been successful in easing the pre-
scription opioid epidemic, they have done little to reduce either past
month abuse or opioid initiation of heroin. In fact, these prescription
opioid-specific interventions may also have contributed, to some extent,
to part of this increase in heroin use. However, our data suggest the
possibility of a stand-alone heroin epidemic that current prescription
opioid-centered interventions will have little to no effect on as the
prescription opioid to heroin pathway is replaced by initiation to
opioids with heroin directly. Clearly, there needs to be a concerted
effort to adjust our prevention, intervention and treatment protocols to
more adequately prepare for an expanding heroin epidemic.

While we need to continue to consider supply-side interventions in
the context of both prescription opioids and heroin, there are several
other factors that need to be taken into account in order to develop an
effective, multi-level policy approach. First, harm-reduction should
play a central role in policy development, including expansion of needle
exchanges, safe injection sites, fentanyl tests strips and Narcan. These
can provide immediate intervention to saving lives, but they must also
be coupled with an increase in evidence-based prevention efforts and
programs that emphasize the high risk of overdose for not only ex-
perienced users, but those who lack any opioid tolerance whatsoever.
Second, expanding access to medication-assisted treatment (MAT) is
both crucial and time-sensitive. A number of provider level barriers
need to be immediately addressed such as training, reimbursement,
access to mental health services, access to specialists for non-addiction
specialists and dissemination of information to dispel myths or bias of
MAT (DeFlavio, Rolin, Nordstrom, & Kazal Jr, 2015; Huhn & Dunn,
2017; Kermack, Flannery, Tofighi, McNeely, & Lee, 2017). Finally, the
demand side is equally, if not more important as a point of interven-
tion/prevention. If individual users’ attraction to an opioid continues to
escalate, it matters not which opioid they’ve taken, but rather what is so
reinforcing about these drugs. Until we recognize this, the number of
people dependent on opioids will continue to rise, as will the devasta-
tion of opioid overdose deaths. Several reports have emphasized the
role mental health treatment and prevention will play in reducing the
opioid epidemic in the long-term (Howe & Sullivan, 2014; Srivastava &
Gold, 2018), but it needs to be stressed that the opioid epidemic is a
layered one, and the policy response must be as well in order to reduce
the current opioid epidemic in the United States.

Role of funding source

Contributors

Conflict of interest

Author Cicero serves as a consultant on the Scientific Advisory
Board of the non-profit post-marketing surveillance system, RADARS®.
Authors Ellis and Kasper have no conflicts of interest to report.

Acknowledgements

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Increased use of heroin as an initiating opioid of abuse: Further considerations and policy implications

Summary of previous article
Methods for original, updated and supplemental data
Results
Heroin as an initiating opioid
Opioid treatment admissions
Discussion
Overview of results
Importance of increases in heroin use
Policy implications
Role of funding source
Contributors
Conflict of interest
Acknowledgements
References

Increased use of Heroin as an Initiating Opioid of Abuse Further Consideration & Policy Implications

Theodore J. Cicero, PhD; Matthew S. Ellis, and Zachary A. Kasper

Addictive Behaviors, 87, (20

8), p. 267-271

Cicero et al. (2018)
Cicero et al. (2018) sought to update and expand a 2017 survey that reported a marked increase in the use of heroin as an initiating opioid in nontolerant, first time opioid users
Also, Cicero et al. (2018) added a discussion of the policy implications on the overall opioid epidemic
Before we discuss the Cicero et al. (2018) work we must review what are opioids and the 2017 paper that is the basis of the 2018 research
2

Aside: Opiate, Opium, Opioid and Heroin
3

Opiate, Opium, & Opioid
Opiate is a term classically used in pharmacology to mean a drug derived from opium. Wikipedia
Opium is a substance that is derived by collecting and later drying the milky juice that comes from the seed pods of the poppy plant (http://opium.com/what-is-opium/)
Opioid, a more modern term, is used to designate all substances, both natural and synthetic, that bind to opioid receptors in the brain. Wikipedia
4

More on Opioids
Opioids are substances that act on opioid receptors to produce morphine-like effects. Wikipedia
Medically they are primarily used for pain relief, including anesthesia. Wikipedia
Drugs in the class: Morphine, Tramadol, Oxycodone, Fentanyl, etc.
Opioids are also frequently used non-medically for their euphoric effects or to prevent withdrawal
5

Heroin
Heroin, also known as diamorphine among other names, is an opiate most commonly used as a recreational drug for its euphoric effects. Medically it is occasionally used to relieve pain and in opioid replacement therapy. Wikipedia

Heroin is an opioid drug made from morphine, a natural substance taken from the seed pod of the various opium poppy plants grown in Southeast and Southwest Asia, Mexico, and Colombia. Heroin can be a white or brown powder, or a black sticky substance known as black tar heroin. (https://www.drugabuse.gov/publications/drugfacts/heroin)
6

Oxycodone and Hydrocodone
Oxycodone is used to relieve moderate to severe pain. Oxycodone extended-release tablets and extended-release capsules are used to relieve severe pain in people who are expected to need pain medication around the clock for a long time and who cannot be treated with other medications.
Oxycodone may be habit-forming.
Hydrocodone is used to relieve severe pain. Hydrocodone is only used to treat people who are expected to need medication to relieve severe pain around-the-clock for a long time and who cannot be treated with other medications or treatments.
Hydrocodone can be habit forming, especially with prolonged use.
URL: https://medlineplus.gov/druginfo/meds/a682132.html#why

Cicero et al. (2017): Substratum Paper for Cicero et al. (2018)
8

Cicero et al. (2017) SKIP and RADARS® and Funding
The national survey data were collected from a subset of participants from the Survey of Key Informants’ Patients (SKIP) Program, a component of the RADARS®
The RADARS (Researched Misuse, Diversion and Addiction-Related Surveillance) System is supported by subscriptions from pharmaceutical manufacturers for surveillance, research and reporting services.
9

SKIP in More Detail
The SKIP (Survey of Key Informants’ Patients) program was developed in order to examine the abuse, misuse, and diversion of opioid drugs within the United States and Canada, and is conducted by Dr. Theodore J Cicero, PhD of Washington University in Saint Louis.
According to Cicero et al. (2014), “[t]he SKIP Program consists of more than 150 publicly and privately funded treatment centers…, balanced geographically with coverage in 48 states, that recruit patients/clients to complete an anonymous survey” (p. E2)
Participants must be 18 years of age or older and must meet DSM-IV criteria for substance abuse with a primary drug that is an opioid (prescription drug or heroin).
Key Informants at substance abuse programs (e.g., addiction specialist, counselor, or other relevant staff member) will be responsible for distributing the questionnaire to willing patients who report having abused any prescription opioid or heroin in the 30 days prior to being admitted into treatment.
10

Stylized Research Question & Hypothesis
The below hypothesis has been restated from the original to comply with figure 1 (Cicero et al., 2017, p.64).
Research Question:
Has there been an increase in the use of heroin as a first opioid of regular use?
Main Research Hypothesis:
An increasing number of persons with limited experience and tolerance to opioids will experiment with heroin as their first opioid of regular use rather than more common, but less accessible, prescription opioid analgesics.
11

Rehash of Cicero et al. (2017) Hypothesis
Main Hypothesis:
“…an increasing number of persons inexperienced with opioids might begin to experiment with readily available heroin as their first opioid of abuse, rather than a less risky, but also less accessible prescription opioid” (Cicero et al., 2017, p. 64).
Corollary hypothesis (not tested):
“Given the imprecision in titrating doses and the potential for potent adulterants (e.g., fentanyl analogues), we anticipated that, should our hypothesis be supported, this emerging trend could very well be associated with an increase in heroin related overdose fatalities, particularly in novice opioid users who lack the degree of tolerance found in more experienced ones” (Cicero et al., 2017, p. 64).
12

Main Variables in Cicero et al. (2017)
Independent Variables: Year Beginning Regular Use of Opioid(s),
Demographic variables: Age, Gender, Ethnicity,
Urbanicity of residence, Highest Education Completed
Dependent Variables: Percent of Opioid Initiators (cf, Figure 1, p. 64)
Drug: Heroin vs. Prescription opioid (cf, Table 1, p.65)
Opioid of first regular use (i.e., 2+ times a month):
hydrocodone, oxycodone, heroin, or other prescription
opioids (buprenorphine, fentanyl, hydromorphone,
methadone, morphine, oxymorphine, tapentadol, and
tramadol)
13

Year Beginning Regular Use
Cicero et al. (2017) employed Questions, 1, 3, and 28 to compute “Year Beginning Regular Use”

Sample & Data Collection
Cicero et al. (2017) analyzed data on opioid use initiation patterns (i.e., first opioid regularly used) using structured, self-administered surveys in opioid-dependent patients (N = 5,885) entering one of over 150 substance abuse treatment programs around the country from 2011 to 2016.
Essentially Cicero et al. (2017) used non-probability network sample with 150 treatment programs as the primary sampling unit and within each program Key Informants (treatment program staff) recruit substance abuse treatment patients into the sample (secondary sampling unit).
Participants were volunteers 18 years of age or older and met DSM-IV criteria for substance abuse with a primary drug that is an opioid (prescription drug or heroin).
Clients were asked by treatment center staff to complete an anonymous paper survey centered on opioid abuse patterns and related behaviors, with an 85% response rate
The survey packet included a $20 Wal-Mart gift card and a self-addressed stamped envelope which, after completion, was used by the respondent to mail the survey (identified by a unique case number) directly to Washington University in St. Louis.
15

Hedging Against Two Biases: Recall and Survival
To limit long-term recall and survival bias, analyses was restricted to opioid initiation that occurred in the past ten years (2005–2015); this period was deemed to manifest stable recall among respondents for opioid abuse (Shillington et al., 1995).
Recall bias is a systematic error that occurs when participants do not remember previous events or experiences accurately or omit details: the accuracy and volume of memories may be influenced by subsequent events and experiences (https://catalogofbias.org/biases/recall-bias)
Survival bias is a type of selection bias that occurs when the selection process of a study favors certain individuals who made it past a certain obstacle or point in time (viz., entry into opioid abuse treatment) and ignores the individuals who did not (e.g., individual overdose early into their heroin use history), https://first10em.com/ebm/survival-bias/.
16

Statistical Analysis
To determine whether there were significant changes in first opioid of regular use over time, Cicero et al. (2017) used Cochran-Armitage test for trend (cf, Fig 1, p. 64)
The Cochran-Armitage test can be used to test the following two-sided hypothesis for i = the type of first opioid of regular use:
H0: p2005, i = p2006, i = … = p2015, i
vs.
H1: p2005, i < p2006, i < ... < p2015, i or p2005, i > p2006, i > … > p2015, i
Chi-square tests of independence were used to assess differences in demographic characteristics between heroin and prescription opioid initiates, cf, table 1
17

18
Figure 1

Key Findings of Cicero et al. (2017)
20
In 2005, 8.7% of opioid initiates began with heroin; this rose to 33.3% in 2015.
In 2005, 42.4% of initiates began with hydrocodone and 42.3% with oxycodone;
this fell to 24.1% and 27.8%, respectively, in 2015.
Individuals who initiated with heroin were younger and less likely to have a college
education, be white, or reside in non-urban areas, but the differences were relatively small.
In fine, Cicero et al. (2017) concluded that there was a significant increase in the number of treatment-seeking opioid users whose first experience with an opioid was with heroin, rather than the more recently commonplace pattern of initiating opioid (e.g., oxycodone or hydrocodone)

Study Limitations
This study is limited by its focus on individuals seeking treatment and may not be representative of others with opioid use.
The study is retrospective and such a study suffers potential issues of recall
In addition, the time lag between onset of use and treatment entry resulted in a decrease in Ns available for recent years, which could impact the generalizability of our findings.
Additionally, it needs to be stressed that Cicero et al. (2017) results potentially underestimate the extent of harm to those who initiate regular opioid use with heroin – so called survival bias (i.e., those who overdosed early into their heroin use would obviously not be included in these studies).
Sample is a treatment-based sample and hence the results are not representative of those who use opioids “recreationally.”
Furthermore, differences in the factors influencing the decision to enter treatment, such as family/court pressures and financial ability, could limit the heterogeneity of the sample.
21

Future Directions
Future studies should examine, at a deeper level, the potential impact of these confounders on treatment-seeking behavior.
Cicero et al. (2018) felt the need to update their data after the original article (Cicero et al. 2017) went to press in order to validate their original findings and underscore the importance of the ‘treatment gap’ in interpreting the data.
Cicero et al. (2018) examined the role of the `treatment gap’, refers to the time lag between initial opioid exposure and treatment admission, which accounted for the lower overall numbers in more recent years.
Additionally, Cicero et al. (2018) sought to update and expand upon these results, with a discussion of the policy implications on the overall opioid epidemic

Cicero et al. (2018)
23

Cicero et al. (2018) ABSTRACT
Introduction: Previously, Cicero et al. reported a marked increase in the use of heroin as an initiating opioid in nontolerant, ﬁrst time opioid users. In the current paper, Cicero et al. sought to update and expand upon these results, with a discussion of the policy implications on the overall opioid epidemic.
Methods: Opioid initiation data from the original study were updated to include surveys completed through 2017 (N=8,382) from a national sample of treatment-seeking opioid users. In addition, past month abuse of heroin and prescription were analyzed as raw numbers of treatment program entrant in the last ﬁve years (2013–2017), drawing from only those treatment centers that participated every year in that time frame.
Results: The updated data conﬁrm and extend the results of our original study: the use of heroin as an initiating opioid increased from 8.7% in 2005 to 31.6% in2015, with increases in overall Ns per initiation year reﬂecting a narrowing of the “treatment gap”, the time lag between opioid initiation from 2005 to 2015 and later treatment admission (up to 2017). Slight decreases were observed in treatment admissions, but this decline was totally conﬁned to prescription opioid use, with heroin use continuing to increase in absolute numbers.
Conclusions: Given that opioid novices have limited tolerance, the risk of fatal overdose for heroin initiates is elevated compared to prescription opioids, particularly given non-oral administration and often unknown purity/adulterants (i.e., fentanyl). Imprecision of titrating dose among opioid novices may explain observed increases opioid overdoses. Future policy decisions should note that prescription opioid-speciﬁc interventions may have little impact on a growing heroin epidemic.
24

Raison d’etre for the 2018 Paper
The authors’ noted that there were some ambiguities in their data given the relatively low numbers of individuals represented in more recent years for which they had data.
Cicero et al. (2018) felt the need to update their data after the original article went to press in order to validate their original findings and underscore the importance of the ‘treatment gap’ in interpreting our data.
The treatment gap refers to, there is a time lag between initial opioid exposure and treatment admission, which accounted for the lower overall numbers in more recent years.
The data have been updated for this report using the same criteria and analysis period, with the inclusion of data from surveys received through the end of 2017.
25

OBSERVATIONAL DESIGN: SURVEY & DATA COLLECTION
Research Strategy: Survey
Using a survey methods approach, Cicero et al. (2018) analyzed data from and updated an ongoing study to include questionnaires completed in 2017 that uses structured, self-administered questionnaires to gather retrospective data on past drug use patterns among patients entering substance abuse treatment programs across the country who received a primary (DSM-IV) diagnosis of heroin use/dependence (sample size, n = 8,382, cf, figure 1)
Past month abuse of heroin and prescription were analyzed as raw numbers of treatment program entrants in the last ﬁve years (2013–2017), drawing from only those treatment centers that participated every year in that time frame (n = 66 programs, cf, figure 2).
Data Collection: Self-administered Questionnaire
Structured interview is a type of interview with highly specific objectives in which all questions are written beforehand and asked in the same order for all respondents, and the interviewer’s responses, and the interviewer’s remarks are standardized
A self-administered questionnaire refers to a questionnaire that has been designed specifically to be completed by a respondent without intervention of the researchers (e.g. an interviewer) collecting the data (http://methods.sagepub.com/reference/encyclopedia-of-survey-research-methods/n522.xml).
26

Revised Analysis of Survey of Key Informants’ Patients (SKIP) Data
27

Figure 1: Heroin as an Initiating Opioid Increased from 8.7% in 2005 to 31.6% in 2015
28

Figure 1 (cf, p. 268)
Fig. 1 shows trendlines from the original published figure, represented as dotted lines, and updated trendlines (solid line).
The lower numbers on the x axis represent the original N at each time point whereas the upper numbers shows the increase in Ns after the inclusion of data through 2017 (N=8,382).
As can be seen, there was a considerable increase in numbers as data has accumulated, but the trends remained the same.
Heroin use as a first opioid grew sharply from 8.7% of the sample in 2005 to almost 31.6% in 2015. It should be noted that the numbers in more recent years increased due to the addition of new data bridging the treatment gap.
That is, there is a time gap between initial opioid exposure which occurred from 2005 to 2015 and treatment admission from 2011 to 2017, which accounts for the lower overall numbers in more recent years.
But, as this figure demonstrates, as data continue to be collected, these overall numbers will continue to increase as the treatment gap narrows.
29

Fig 2: The Number of Patients Entering 66 Treatment Centers Decreased (From 1,598 to 1,421) Modestly Over Time Due to Yearly Decline in Past Month Prescription Opioid Abuse
30

Figure 2
Cicero et al. (2018) examined the raw number of patients entering into a panel of 66 treatment clinics participating in the SKIP program in the last five years who indicated past month abuse of heroin and/or a prescription opioid (p. 269).
The total number of those entering our stable sample of treatment centers decreased modestly over time (from 1,598 to 1,421) implying a decrease in overall opioid use if viewed as a whole (cf, p. 269).
However, this overall decrease was solely attributable to decreases in past month prescription opioid abuse (from 1,453 to 1,197)
While participants could endorse both prescription opioids and heroin, the data is presented separately in order to depict the total number of users for each drug category.

Findings and Conclusions for Cicero et al. (2018)
32

Key Findings of Cicero et al. (2018)
Heroin as the first opioid of abuse has grown significantly in the past decade
Past month heroin use continues to grow as prescription opioid abuse declines
33

Policy Implications
“Our data indicate that while supply reduction efforts targeting prescription opioid abuse may have been successful in easing the prescription opioid epidemic, they have done little to reduce either past month abuse or opioid initiation of heroin. In fact, these prescription opioid-specific interventions may also have contributed, to some extent, to part of this increase in heroin use. However, our data suggest the possibility of a stand-alone heroin epidemic that current prescription opioid-centered interventions will have little to no effect on as the prescription opioid to heroin pathway is replaced by initiation to opioids with heroin directly. Clearly, there needs to be a concerted effort to adjust our prevention, intervention and treatment protocols to more adequately prepare for an expanding heroin epidemic” (Cicero et al., 2018, p. 270).
Bottom line: “Future policy decisions should note that prescription opioid-specific (Cicero et al., 2018, p.267).

References
Cicero, T. J. et al. (2014). The Changing Face of Heroin Use in the United States A Retrospective Analysis of the Past 50 Years.
JAMA Psychiatry, p. E1 – E6.
Cicero, T. J. et al. (2017). Increased use of Heroin as an Initiating Opioid of Abuse. Additive Behaviors, 74, 63-66.
Cicero, T. J. et al. (2018). Increased use of Heroin as an Initiating Opioid of Abuse: Further Considerations and Policy
Implications. Additive Behaviors, 87, 267-271.

http://opium.com/what-is-opium/

https://www.drugabuse.gov/publications/drugfacts/heroin

https://en.wikipedia.org/wiki/Opiate

https://en.wikipedia.org/wiki/Opioid

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