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TOWARD QUANTIFYING THE ABUSE LIABILITY OF ULTRAVIOLET TANNING:
A BEHAVIORAL ECONOMIC APPROACH TO TANNING ADDICTION
DEREK D. REED
1
,BRENT A. KAPLAN
1
,AMEL BECIREVIC
1
,PETER G. ROMA
2
,
AND STEVEN R. HURSH
2
1
UNIVERSITY OF KANSAS
2
INSTITUTES FOR BEHAVIOR RESOURCES AND JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE
Many adults engage in ultraviolet indoor tanning despite evidence of its association with skin cancer.
The constellation of behaviors associated with ultraviolet indoor tanning is analogous to that in other
behavioral addictions. Despite a growing literature on ultraviolet indoor tanning as an addiction, there
remains no consensus on how to identify ultraviolet indoor tanning addictive tendencies. The purpose
of the present study was to translate a behavioral economic task more commonly used in substance
abuse to quantify the "abuse liability" of ultraviolet indoor tanning, establish construct validity, and
determine convergent validity with the most commonly used diagnostic tools for ultraviolet indoor tan-
ning addiction (i.e., mCAGE and mDSM-IV-TR). We conducted a between-groups study using a novel
hypothetical Tanning Purchase Task to quantify intensity and elasticity of ultraviolet indoor tanning
demand and permit statistical comparisons with the mCAGE and mDSM-IV-TR. Results suggest that
behavioral economic demand is related to ultraviolet indoor tanning addiction status and adequately
discriminates between potential addicted individuals from nonaddicted individuals. Moreover, we pro-
vide evidence that the Tanning Purchase Task renders behavioral economic indicators that are relevant
to public health research. The present findings are limited to two ultraviolet indoor tanning addiction
tools and a relatively small sample of high-risk ultraviolet indoor tanning users; however, these pilot data
demonstrate the potential for behavioral economic assessment tools as diagnostic and research aids in
ultraviolet indoor tanning addiction studies.
Key words: behavioral addiction, behavioral economics, demand, hypothetical purchase task, tanning
The subdiscipline of behavioral economics
in behavior analysis initially provided a theo-
retical account of operant responding in vary-
ing forms of reinforcer economies (Hursh,
1980, 1984; Hursh & Roma, 2016; Kagel &
Winkler, 1972). Continued research has ren-
dered behavioral economics a viable approach
to understanding reinforcer value and con-
sumption under constraining conditions such
as closed economies, price increases, and
effort manipulations (Kagel, Battalio, &
Green, 1995). The analysis of reinforcer con-
sumption under constraining conditions gave
rise to a novel means of understanding drug
self-administration, which researchers quickly
translated to human models of addiction and
dependence (Bickel, DeGrandpre, & Higgins,
1993; Bickel, DeGrandpre, Higgins, & Hughes,
1990; Bickel, DeGrandpre, Hughes, & Higgins,
1991; Bickel, Hughes, DeGrandpre, Higgins, &
Rizzuto, 1992; Bickel, Madden, & Petry, 1998;
DeGrandpre, Bickel, Hughes, & Higgins, 1992;
Hursh, 1993). Contemporary behavioral eco-
nomics is now widely adopted—both within
and outside of behavior analysis—as a robust
conceptual, methodological, and analytical
framework for behavioral addictions and sub-
stance use disorders (Bickel, Jarmolowicz,
Mueller, & Gatchalian, 2011; Bickel, Johnson,
Koffarnus, MacKillop, & Murphy, 2014;
Carter & Griffiths, 2009; Jarmolowicz, Reed,
DiGennaro Reed, & Bickel, 2016; MacKillop,
2016) and behavioral health, in general
(Bickel & Vuchinich, 2000).
Much of the contemporary success in behav-
ioral economics is due to its scalability in
informing public policy (Hursh & Roma,
2013) and utility in identifying potential abuse
liability of particular commodities
(Christensen, Silberberg, Hursh, Huntsberry, &
Riley, 2008; Hursh & Winger, 1995). Toward
this end, behavioral economists have begun to
rely on hypothetical purchase tasks (see Roma,
Hursh, & Hudja, 2016) to simulate consump-
tion as a means of safely and quickly quantify-
ing abuse liability (Jacobs & Bickel, 1999;
Murphy & MacKillop, 2005; Murphy & MacKil-
lop, 2006) and informing effective policies to
Address correspondence to: Derek D. Reed, Ph.D., 4048
Dole Human Development Center, 1000 Sunnyside Ave-
nue, Lawrence, KS, 66045–7555. Phone: 785.864.0504.
Fax: 785.864.5202. E-mail: dreed@ku.edu.
doi: 10.1002/jeab.216
JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2016, 1–14
1
reduce dependence (MacKillop et al., 2012).
The hypothetical purchase task is used to
quantify demand and demand elasticity for a
commodity, most typically drugs of abuse
(e.g., alcohol, tobacco, heroin), whereby indi-
viduals report how many units of a commodity
they would purchase or the probability of a
single purchase across a wide range of prices
(Roma et al., 2016). Beyond being an efficient
analog to drug administration, hypothetical
purchase tasks feature sufficient psychometrics
such as convergent/divergent validity
(MacKillop et al., 2008; Murphy, MacKillop,
Tidey, Brazil, & Colby, 2011) and temporal sta-
bility (Amlung & MacKillop, 2012; Few, Acker,
Murphy, & MacKillop, 2011; 2012). Although
these hypothetical purchase tasks demonstrate
adequate psychometric properties for tradi-
tional drugs of abuse (see also: Amlung,
Acker, Stojek, Murphy, & MacKillop, 2012;
Kiselica, Webber, & Bornovalova, 2016; MacK-
illop, 2016; MacKillop & Murphy, 2007; Reed,
Kaplan, & Becirevic, 2015), the degree to
which hypothetical purchase tasks are translat-
able to under-researched behavioral addic-
tions remains relatively unknown. The
purpose of this translational study was to
examine the degree to which a hypothetical
purchase task for ultraviolet indoor tanning
(UVIT) corresponds with known markers of
UVIT dependence used in clinical dermatology.
Over 10 million Americans are estimated to
use UVIT annually (Guy, Berkowitz, Hol-
man, & Hartman, 2015), with the highest prev-
alence among Caucasian college-aged females
(aged 18–21; 31.6%) who report 27.6 UVIT
events per year, on the average (Centers for
Disease Control, 2012). These statistics suggest
relatively intense consumer demand for UVIT
in this population, which is troubling given
clear and widely publicized reports that UVIT
before the age of 25 increases one’s risk of
skin cancer by up to 102% (Wehner et al.,
2012). Such substantial demand for UVIT
likely results from a host of contributing fac-
tors. Curious potential UVIT users are subject
to a multi-billion-dollar industry (Bizzozero,
2009) with advertising techniques that health
professionals have likened to those used in the
tobacco industry (Holman et al., 2013). After
trying UVIT, some users may experience pleas-
urable consequences by way of physiological
sensations, relaxation, social praise, percep-
tions of increased attractiveness, and other
reinforcement mechanisms (Cafri et al., 2006;
Feldman et al., 2004; Hillhouse & Turrisi, 2012;
Kaur, Liguori, Fleischer, & Feldman, 2006;
Schneider et al., 2013; Warthan, Uchida, &
Wagner, 2005). Indeed, Feldman and collea-
gues (2004) documented a potential reinforce-
ment effect of UVIT by demonstrating that
regular UVIT users exposed to both a typical
UV sunbed and an identical sunbed containing
aUVfilter will use the sunbed emitting UV,
ceteris paribus, despite these users being blind to
which sunbed contained the filter. Thus,
UV radiation may be considered a consumable
substance, considering that exposure to UV radi-
ation in UVIT is associated with cutaneous
opioid release (Kaur, Liguori, Fleischer et al.,
2006; Kaur, Liguori, Lang et al., 2006).
The notion of UVIT dependence has been
substantiated by studies drawing parallels
between UVIT and substance-related disorders
(Ashrafioun & Bonar, 2014; Mosher & Danoff-
Burg, 2010). Academic dermatologists report
many UVIT users exhibit substantial difficulty
in abstaining from UVIT, as well as physiologi-
cal withdrawal symptoms upon abstinence ini-
tiation, regardless of their initial motives to
tan (Ashrafioun & Bonar, 2014; Harrington
et al., 2011; Nolan, Taylor, Liguori, & Feld-
man, 2009). Reports of such dependence
have led to a proliferation of discussions on
UVIT as a commodity with abuse liability,
akin to other substance-related disorders
(Ashrafioun & Bonar, 2014; Harrington
et al., 2011; Nolan et al., 2009). Moreover, the
maladaptive choices (Bickel et al., 2011,
Bickel, Jarmolowicz, MacKillop et al., 2012;
Bickel, Jarmolowicz, Mueller et al., 2012;
Bickel et al., 2014; Jarmolowicz et al., 2016)
associated with UVIT dependence appear simi-
lar to other behavioral addictions (see discus-
sion by Reed, 2014).
Despite developments in tanning-specific
addiction assessment tools (Heckman et al.,
2014; Hillhouse, Turrisi, Stapleton, & Robin-
son, 2010; Schneider et al., 2015), many UVIT
addiction studies have relied upon tools ini-
tially developed for smoking/drinking pro-
blems or behavioral addictions; notably, the
CAGE (Cut-down, Annoyed, Guilty, Eye-
opener) and the substance-related disorder
diagnostic criteria from the Diagnostic and Sta-
tistical Manual of Mental Disorders (Fourth Edi-
tion, Text Revision; DSM-IV-TR; American
Psychiatric Association, 2000; Ashrafioun &
DEREK D. REED et al.2
Bonar, 2014; Cartmel et al., 2013; Harrington
et al., 2011; Heckman, Egleston, Wilson, &
Ingersoll, 2008; Mosher & Danoff-Burg, 2010;
Poorsattar & Hornung, 2007; Schneider et al.,
2013; Warthan et al., 2005). For example, Ash-
rafioun and Bonar surveyed 533 university stu-
dents and found that 31% of the sample met
criteria for tanning dependence according to
a tanning-specific modification of the CAGE
(see below), as well as significant correlations
between tanning dependence and obsessive-
compulsive and body dysmorphic disorders.
Mosher and Danoff-Burg found that 39% of
229 university students met tanning depend-
ence criteria according to the CAGE, and 31%
met dependence criteria according to tanning-
specific modified DSM-IV-TR criteria (see
below). Additionally, participants with tanning
dependence profiles reported significantly
greater prevalence of anxiety and consump-
tion of other commodities with abuse liability
(e.g., marijuana, alcohol), in line with Bickel
and colleagues’(Bickel, Jarmolowicz, MacKil-
lop et al., 2012; Bickel, Jarmolowicz, Mueller
et al., 2012) view that risky health decisions
favoring immediate reinforcers (e.g., UVIT for
short-term gains despite long-term health
impacts) may be a transdisease process under-
lying addiction and spanning a range of addi-
tional clinical disorders.
Initially designed for alcohol use problems,
the CAGE is composed of four criteria
(Aertgeerts, Buntinx, & Kester, 2004): whether
individuals (1) report being told to Cut-down
use of the commodity, (2) feel Annoyed when
others comment about their consumption of
the commodity, (3) have felt Guilty for con-
suming the commodity, and (4) use the com-
modity as an Eye-opener (i.e., use the
commodity upon waking in the morning).
Affirming at least two criteria is regarded as an
indication of potential dependence. A modi-
fied CAGE (mCAGE) assessment has subse-
quently become one of the more commonly
used screeners for UVIT dependence
(Schneider et al., 2015). However, Schneider
and colleagues exposed a number of limita-
tions regarding its use in UVIT contexts:
(a) the clinical utility of the mCAGE for UVIT
has been untested, (b) potential sampling pro-
blems, and (c) lack of no-UVIT controls.
These authors demonstrated a larger and
more representative sample of UVIT users did
not replicate findings from earlier studies
employing the mCAGE; rather, the mCAGE
over-predicted UVIT dependence.
Similar to the translation of CAGE for UVIT
purposes, researchers have adapted the lan-
guage from the substance-related disorders
diagnostic criteria in the Diagnostic and Statisti-
cal Manual of Mental Disorders (Fourth Edition,
Text Revision; DSM-IV-TR; American Psychiatric
Association, 2000). The initial translation of
these substance-related disorders criteria for
UVIT has become the standard language for
use in identifying potential UVIT dependence
(Warthan et al., 2005). As per standard DSM-
IV-TR criteria, affirming at least three of the
seven criteria is regarded as an indication of
potential dependence.
Because “no gold standard for categorizing
tanning dependence has yet been established”
(Schneider et al., 2015), the purpose of this
study was to compare the degree of depend-
ence on the more commonly used clinical
screeners for UVIT dependence (i.e., mCAGE
and mDSM-IV-TR) to contemporary behavioral
economic indicators of substance related dis-
orders (behavioral economic demand via
hypothetical purchase task) using recent, non-
recent, and never UVIT users. Specifically, we
explored the relation between mCAGE and
mDSM-IV-TR ratings to behavioral economic
demand (Bickel et al., 1998; Hursh, 1993) for
UVIT. Behavioral economic demand is a quan-
titative marker of how consumers defend con-
sumption of a given commodity as the price of
the commodity increases. For example, a
behavioral economist assesses the degree to
which a consumer consumes a commodity
under ideal market pressures (e.g., very low
costs to the consumer). The behavioral econo-
mist also measures the elasticity of demand by
quantifying change in consumption as a func-
tion of varying costs. Relatively inelastic
demand—that is, stable consumption that is
insensitive to price increases—indicates
stronger demand for the commodity and
stronger underlying reinforcing effectiveness.
Demand elasticity also informs additional
quantitative markers of demand, such as P
max
(the price associated with unit elasticity where
one-unit increase in price is met with one-unit
decrease in consumption), O
max
(consumption
at P
max
), and essential value (EV; a standar-
dized metric based on an elasticity rate con-
stant that succinctly summarizes an organism’s
consumption of the target commodity).
Behavioral economic demand for commod-
ities of abuse serves as a major component in
3BEHAVIORAL ECONOMIC ANALYSIS
the reinforcer pathologies model (Bickel,
Jarmolowicz, MacKillop et al., 2012; Bickel,
Jarmolowicz, Mueller et al., 2012; Bickel et al.,
2014), which is a theoretical approach in the
understanding and treatment of substance-
related disorders grounded in behavior sci-
ence. Specifically, stronger demand is consid-
ered a marker of potential substance-related
disorder. The degree of demand inelasticity
for a commodity is thereby associated with that
commodity’s abuse liability. Validating behav-
ioral economic measures of abuse liability
using common diagnostic tools for UVIT may
provide insight on the validity of our transla-
tion of the hypothetical purchase task for this
novel behavioral addiction. Demonstrating a
conceptually systematic relation between
demand and UVIT abuse will enable addi-
tional clinical research on the behavioral eco-
nomic components of UVIT use and
potentially inform novel treatment
procedures.
Methods
Participants
A total of 102 undergraduate females
enrolled in an introductory psychology course
were recruited and received extra credit for
participating. The final sample for analysis
contained 93 female participants between the
ages of 18 and 36 (mean age = 20.19 years
[SD = 2.87]) after excluding participants based
on response patterns on the behavioral eco-
nomic task (see criteria below). More than
simply a convenience sample, this demo-
graphic represents the population at the high-
est risk of UVIT-induced cancer (Karagas
et al., 2002; Wehner et al., 2012). All data were
collected two weeks prior to the university’s
scheduled spring break, which is a temporal
event associated with increased tanning rates
on college campuses (Heckman et al., 2008).
An information statement consent process
entailed a written cover letter explaining the
broad goals of the study, along with the risks
(i.e., boredom), benefits (i.e., increased
understanding of consumer choices), and par-
ticipant rights (i.e., voluntary participation,
anonymity of results) associated with participa-
tion. The information statement explained
that subsequent completion of the paper-
based task (see below) was an indication of
written consent. This consent process permits
further anonymity and confidentiality as no
participant names are directly linked to their
responses. All procedures, including the infor-
mation statement consent process, were
approved by the Human Subjects Committee
of the University of Kansas Lawrence Campus
(#20635).
Procedures
Participants completed the mCAGE and
mDSM-IV-TR to determine potential depend-
ence to UVIT. For the mCAGE (Mosher &
Danoff-Burg, 2010; Warthan et al., 2005),
affirmative responses to at least two of the fol-
lowing four statements resulted in a positive
classification, whereas affirmative responses to
less than two of the following four questions
resulted in a negative classification:
1. Do you try to cut down on the time you
spend in tanning beds or booths?
2. Do you ever get annoyed when people tell
you not to use tanning beds or booths?
3. Do you ever feel guilty that you are using
tanning beds or booths too much?
4. When you wake up in the morning, do
you want to use a tanning bed or booth?
The following seven diagnostic criteria com-
prising the mDSM-IV-TR (American Psychiatric
Association, 2000; Mosher & Danoff-Burg,
2010; Warthan et al., 2005) were scored identi-
cally to past studies (Ashrafioun & Bonar,
2014; Mosher & Danoff-Burg, 2010; Warthan
et al., 2005):
1. (a) Do you think you need to spend
more and more time in tanning beds
or booths to maintain your per-
fect tan?
(b) Do you think your tan will fade if
you spend the same amount of time
in a tanning bed or booth
each time?
2. Do you continue to use tanning beds or
booths so your tan will not fade?
3. When you go to tanning salons, do you
usually spend more time in the tanning
bed or booth than you had planned?
4. Do you try other non-tanning-related activ-
ities, but find you really still like spending
time in tanning beds or booths best of all?
5. (a) How many days a week do you spend
in tanning beds or booths?
DEREK D. REED et al.4
(b) How many days a week do you spend
tanning in the sun?
(c) Do you tan year round?
(d) Have you ever missed work, a social
engagement, or school because of a
burn from tanning bed or
booth use?
6. Have you ever missed any scheduled event
(social, occupational, or recreational activ-
ities) because you decided to use tanning
beds or booths?
7. (a) Do you believe you can get skin can-
cer from the sun?
(b) Do you believe you can get skin can-
cer from tanning beds?
(c) Does this keep you from spending
time in the sun or using tanning
beds or booths?
Specifically, each “yes”response is scored as
one affirmative indicator, with the following
exceptions for items 1, 5, and 7: (1) question
1 is scored as affirmative for “yes”responses to
both 1a and 1b; (2) question 5 is scored as
affirmative for positive responses on at least
2 subparts (responses other than 0 are scored
positive for 5a); and (3) question 7 is scored
as affirmative for a “yes”response to 7a
and/or 7b and a “no”response to 7c. At least
three affirmative responses across the seven
items resulted in a classification of potential
dependence (i.e., positive classification); less
than three affirmative responses resulted in a
classification of no potential dependence
(i.e., negative classification).
We created a composite diagnostic based on
classifications on the mCAGE and mDSM-IV-
TR. Participants who scored positive for poten-
tial dependence on at least one of the two
screening scales (mCAGE and mDSM-IV-TR)
were classified as “At Risk”(n= 42) and those
who scored negative across both scales were
classified as “No Risk”(n= 51).
In addition to completion of the screening
scales, participants reported the time they last
used a UVIT device (Table 1) and were classi-
fied according to the following criteria: Recent
users (N= 37) were classified as using UVIT
within the past month, Non-Recent users
(N= 28) were classified as using UVIT within
the past 5 years but not within the past month,
and Never users (N= 28) were classified as
never using UVIT. No participants reported
their last UVIT use more than 5 years ago.
Note, the total number of participants who
were classified as either “At Risk”or “No Risk”
or as Recent, Non-Recent, or Never users is
less than the original sample size due to exclu-
sionary criteria described in the data analysis
section below.
Finally, to measure behavioral economic
demand for UVIT, participants completed a
tanning purchase task (TPT) developed based
on previous studies using hypothetical pur-
chase tasks (Murphy & MacKillop, 2006; Reed
et al., 2015; Roma et al., 2016). The TPT was
administered via paper and pencil with the fol-
lowing instructions:
“What is the likelihood that you sign up for
a month of the most basic unlimited tan-
ning? Use a value of 0 if you would
NEVER consider signing up at the given
price. Use a value between 0–100 to indi-
cate the extent that you are likely to sign up
at the given price. Use a value of 100 if
you would not hesitate in signing up at the
given price.”
where the given price of tanning was defined
as a base price of $30.00 (approximately 50-
75% the price of local salon packages to
ensure relative inelasticity of demand at low
Table 1
Demographic Characteristics of Study Participants
Characteristic
No. (%) of
93 Participants
Age, years
18-19 42 (45)
20-21 41 (44)
22-23 5 (5)
24-25 2 (2)
>25 3 (2)
Skin type
Burns, never tans 4 (4)
Burns easily, then develops light tan 17 (18
Burns moderately, then develops
light tan
18 (19)
Burns minimally, then develops
moderate tan
32 (34)
Does not burn, develops dark skin 20 (22)
Does not burn, shows no change in
noticeable appearance
1 (1)
N/A 1 (1)
UVIT Use Frequency
Never 28 (30)
Non-Recent (within past 5 years, not
within past month)
28 (30)
Recent (within past month) 37 (40)
5BEHAVIORAL ECONOMIC ANALYSIS
prices in the demand curve) plus a tax ranging
from $0.00 (no tax) to $60.00. The progres-
sion of total price included the following taxes:
$0.00 (no tax), 0.30, 0.60, 1.50, 3.00, 4.50,
6.00, 7.50, 9.00, 12.00, 15.00, 18.00, 22.50,
30.00, and 60.00. Given past research suggest-
ing that most tanning facilities offer unlimited
tanning packages (e.g., Kwon et al., 2002), we
instructed participants to report the likelihood
of purchasing the month of unlimited tanning
by handwriting their numeric response (in %
likelihood) on a blank line to the right of each
total price (i.e., base price plus tax). While fre-
quency of consumption is typically—but not
exclusively—used in hypothetical purchase
tasks (for other examples of demand curves
using likelihood responses, see Henley, DiGen-
naro Reed, Kaplan, & Reed, 2016; Roma et al.,
2016), participants were unlikely to have expe-
rience purchasing single tanning sessions
which may have compromised the integrity of
their responses. The use of progressively
increasing tax prices modeled the effects of
potential excise tax increases on indoor tan-
ning (comparable to the 10% excise tax on
indoor tanning services levied by the Patient
Protection and Affordable Care Act 2010).
Data Analysis
Responses on the TPT were converted to a
proportion (out of 1) and subsequently ana-
lyzed according to the exponential model of
demand (Hursh & Silberberg, 2008) using a
freely available GraphPad Prism
®
template
provided by the Institutes for Behavior
Resources (http://www.ibrinc.org/index.php?
id=181; note that this GraphPad Prism
®
solu-
tion uses standard nonlinear regression to
minimize the sum of squares via the Mar-
quardt method; Marquardt, 1963):
logQ= logQ0+ke−αQ0CðÞ
−1
ð1Þ
where Qis the likelihood of purchase at each
tax price (i.e., C), Q
0
(i.e., Demand Intensity)is
the maximum likelihood of purchase associ-
ated with no tax (converted to $.01 for curve-
fitting purposes), kis the range of consump-
tion in logarithmic units (in this case k=2
due to the absolute range of responses, ran-
ging from .01-1), and αis the rate of change
in elasticity across the demand curve.
Several other behavioral economic metrics
were calculated using freely available software
(Kaplan & Reed, 2014) and equations theo-
rized by Hursh (2014). P
max
is the price at
which likelihood of purchase disproportion-
ately decreases with increases in price
(i.e., slope = −1, unit elasticity), calculated as:
Pmax =:083k+0:65ðÞ
Q0αk1:5
ðÞ ð2Þ
O
max
represents maximum expenditure, but
for the purposes of the current study repre-
sents the expected tax revenue at P
max
, and is
calculated by multiplying P
max
by Qat P
max
.
Finally, essential value (EV)reflects the nor-
malized (i.e., relatively k- and Q
0
–independ-
ent) reinforcing value of the commodity, with
larger EVs signifying relatively higher demand.
Essential value was calculated as:
EV =1
100αk1:5
ðÞ ð3Þ
All demand indices were derived except
Intensity and k(i.e., constrained in
Equation 1). A “0”value was inputted for P
max
,
O
max
, and EV for participants who indicated no
level of consumption at any price. Participants’
data were excluded if their responses derived
negative demand indices (n=1) or if
responses on the TPT contained multiple
instances of consumption value increases
across increasing prices (n= 2). These exclu-
sionary criteria follow the logic of Stein and
colleagues’(Stein, Koffarnus, Snider, Quisen-
berry, & Bickel, 2015) algorithm for identify-
ing nonsystematic data while also permitting
examination of zero demand datasets
(e.g., zero consumption across all prices). Six
additional participants were excluded due to
unusable P
max
values (range = $412,142.20 to
$1.8x10
14
). All unusable P
max
values were arti-
facts of consumption patterns containing a
series of exclusively nondecreasing, nonzero
consumption values (extreme inelasticity)
before reporting complete suppression in con-
sumption throughout the remainder of the
TPT; such patterns return near-zero αvalues
(due to extreme inelasticity), which returns
nonsensical P
max
values when inputted to
Equation 2. Thus, the final sample size after
exclusions was 93. Chi-square analyses were
DEREK D. REED et al.6
conducted using IBM
®
SPSS
®
Statistics Ver-
sion 22.0.0.0 (64-bit; Windows) and all other
statistical tests were conducted using Graph-
Pad Prism
®
7.00 for Windows.
Results
A Pearson χ
2
analysis was used to examine
the relation between mCAGE and mDSM-IV-
TR and between frequency and diagnostic
status. Results indicate a significant relation
between mCAGE and mDSM-IV-TR,
χ
2
(1, N= 93) = 7.353, p= .007; d= .281, repli-
cating previous findings (Mosher & Danoff-
Burg, 2010; Warthan et al., 2005). Among the
30 participants who scored positive on only
one of the two diagnostic tools, 24 scored
positive on the mCAGE and only six scored
positive on the mDSM-IV-TR. The relation
between usage status and diagnostic status
(Table 2) was also significant, χ
2
(2, N= 93) =
34.101, p< .0001; d= .606.
The left panels in Figures 1 and 2 show
Equation 1 provided an excellent fit to the
group data (R
2
=0.96-1.0; RMSE = .033-.096),
regardless of categorization or status. For indi-
vidual comparisons, participants’behavioral
economic indices were graphed and descrip-
tive statistics were obtained to determine distri-
bution shape. Kruskal-Wallis and Dunn’s
multiple comparisons tests were performed to
compare demand indices across the three fre-
quency categorizations (right panel of Fig. 1).
Each significance level (alpha) was adjusted to
account for multiple comparisons. Multiple
comparisons yielded significant differences for
all of the following measures at p< .02. For
EV, results showed significant differences
between the three groups, H(2, N= 93) =
38.67, p< .0001. Comparisons for Intensity
also showed significant differences,
H(2, N= 93) = 34.11, p< .0001. Finally, signif-
icant differences were observed for P
max
(H[2,
N= 93] = 38.08, p< .0001) and O
max
(H[2,
N= 93] = 38.67, p< .0001). These results sug-
gest groups of participants with more recent
UVIT use exhibit significantly greater UVIT
demand than groups with less recent or no
lifetime use.
Because data did not approximate a normal
distribution and we hypothesized At-Risk indi-
viduals would display stronger demand for
UVIT, nonparametric one-tailed Mann–
Whitney Utests were performed to compare
diagnostic status across indices of demand
(right panel of Fig. 2). For EV, At-Risk indivi-
duals (M
rank
= 60.36) displayed significantly
higher median values than No-Risk individuals
(M
rank
= 36), U= 510, p< .0001. At-Risk indivi-
duals (M
rank
= 61.30) reported significantly
higher Intensity than No-Risk individuals
(M
rank
= 35.23), U= 470.5, p< .0001. For P
max
and O
max
, At-Risk individuals (M
rank
= 60.49;
60.36) showed significantly higher values than
No-Risk individuals (M
rank
= 35.89; 36),
U= 504.5, p< .0001 and U= 510, p< .0001,
respectively. Taken together, results indicate
higher demand for the At-Risk group com-
pared to the No-Risk group.
Finally, we compared correlations between
behavioral economic markers of demand for
the aggregate pool of participants (i.e., regard-
less of UVIT use frequency or dependence risk
status). We used Spearman ρcorrelations given
significant deviations from normality for all
four demand metrics (Q
0
,P
max
,O
max
,EV) based
on the D’Agostino & Pearson omnibus normal-
ity test in GraphPad Prism
®
. Table 3 shows that
all correlations were positive and strong
(ρrange .79 to 1.00) and significant (pvalues
range 5.55 x 10
−21
to 0) with a .05 alpha level.
This finding is to be expected given the param-
eter dependencies and mathematical similari-
ties in calculating these behavioral economic
markers of demand.
Discussion
In the current study, we employed a use-
inspired, translational framework to examine
Table 2
Association Between Frequency and Diagnostic Findings
Composite Diagnostic Status
Frequency No Risk At Risk Total, No. (%)
Never 28 (30) 0 (0) 28 (30)
Non-Recent 12 (13) 16 (17) 28 (30)
Recent 11 (12) 26 (28) 37 (40)
Total, No. (%) 51 (55) 42 (45) 93 (100)
Note. Composite Diagnostic Status (Participants were
classified as No Risk if they scored negative on both
mDSM-IV-TR and mCAGE screening tools; Participants
were classified as At Risk if they scored positive on at least
one of the screening tools); Frequency (Participants were
classified as Never tanners if they had never used UVIT;
Non-Recent tanners if they had used UVIT within the past
5 years but not within the past month; Recent tanners if
they had used UVIT within the past month)
7BEHAVIORAL ECONOMIC ANALYSIS
behavioral economic measures of abuse liabil-
ity of UVIT to validate these measures against
common diagnostic tools used to assess UVIT
dependence (mCAGE and mDSM-IV-TR). Our
discussion highlights the potential benefits
and drawbacks of using the novel TPT in aca-
demic and clinical settings. Additionally,
through the strategic use of an increasing pro-
gression of taxes layered onto a base-price for
a month of unlimited UVIT, findings from the
current study allow for the translation of the
aforementioned behavioral economic mea-
sures to public policy implications (discussed
below).
In the final sample of 93 females, 65 (70%)
reported use of UVIT in the past 5 years, with
37 of these 65 (57%) reporting use within the
last month. Chi-squared analyses suggested a
Fig. 1. Demand indices categorized by frequency of UVIT use. Participants were classified as Never tanners if they
had never used UVIT; Non-Recent tanners if they had used UVIT within the past 5 years but not within the past month;
Recent tanners if they had used UVIT within the past month. Panel A displays Equation 1 fitted to the mean data. Panels
B-E show comparisons between participants’individual behavioral economic indices with error bars indicating median
and 95% confidence interval. Significance levels between comparisons denoted by asterisks.
*p< .05. **p< .01. ****p< .0001
DEREK D. REED et al.8
relation between frequency and diagnostic sta-
tus on the mCAGE and mDSM-IV-TR. Behav-
ioral economic measures of the reinforcing
effectiveness of UVIT were significantly greater
for the At-Risk participants, suggesting some
convergent validity in these novel metrics for
UVIT addiction. Moreover, of the 28 partici-
pants in the Never-use group, none met cri-
teria for an At- Risk status.
In examining the 42 At-Risk participants,
only 12 (29%) scored positive for potential
UVIT dependence on both mDSM-IV-TR and
mCAGE. These 12 participants may constitute
a subsample of UVIT-dependent users, but the
low proportion prohibits meaningful inferen-
tial statistical analyses. Of the remaining 30 par-
ticipants scoring positive on only one of the
diagnostic tools, 24 (80%) were due to
mCAGE scores. This disproportionate number
of participants corroborates Schneider and
colleagues’(2015) critique of the mCAGE’s
tendency to over-predict dependence status in
Fig. 2. Demand indices categorized by composite diagnostic status. Composite diagnostic status based on positive or
negative classifications on the mDSM-IV-TR and mCAGE. Participants were classified as No Risk if they scored negative
on both screening tools. Participants were classified as At Risk if they scored positive on at least one of the screening
tools. Panel A displays Equation 1 fitted to the mean data. Panels B-E show comparisons between participants’individual
behavioral economic indices, with error bars indicating median and 95% confidence interval. For all comparisons,
p< .0001.
9BEHAVIORAL ECONOMIC ANALYSIS
UVIT users. Despite the majority of At-Risk
participants being identified via the mCAGE’s
potentially oversensitive scoring, this group
featured significantly greater behavioral eco-
nomic demand for UVIT than No-Risk
participants.
Demand indices have become contempo-
rary behavioral economic markers (Carter &
Griffiths, 2009; MacKillop, 2016) for
(a) identifying the abuse liability of substances
(Bickel, Jarmolowicz, MacKillop et al., 2012;
Bickel, Jarmolowicz, Mueller et al., 2012;
Bickel, Jarmolowicz, Mueller, & Gatchalian,
2011; Bickel et al., 2014; Bickel et al., 1998;
Hursh, 1991; Hursh & Roma, 2016) and
(b) predicting success of treatment for
substance-related disorders (MacKillop & Mur-
phy, 2007; Madden & Kalman, 2010; McClure,
Vandrey, Johnson, & Stitzer, 2013). We posit
that another potential benefit of hypothetical
purchase tasks is that they are not readily iden-
tifiable diagnostic tools. That is, we speculate
that respondents cannot discern what the task
assesses and may thereby reduce reactivity in
the form of “faking good.”Both the mCAGE
and mDSM-IV-TR use language that indicates
the tools’goals; the Tanning Purchase Task
(TPT) is absent of such language and may
thereby yield more honest responding. Addi-
tional empirical work is obviously necessary to
confirm our assumption. Nevertheless, the
purchase task designed for this study may be
useful to both researchers and clinicians inter-
ested in examining UVIT users’potential
abuse risk.
Because the TPT returns systematic data in
line with behavioral economic theory, this task
may be useful in identifying young adults with
substantial UVIT demand. Because demand
markers generated via similar alcohol
purchase tasks are “theorized to be an impor-
tant recursive etiological marker”for later sub-
stance abuse in young adults (see review in
MacKillop, 2016, p. 677), the TPT may be simi-
larly helpful in identifying potential clinical
UVIT dependence in this young adult popula-
tion. Identifying excessive demand as an etio-
logical marker for dependence may be
particularly important for UVIT given the
long-term disease trajectory associated with
cumulative use, especially in young adults
(Zhang et al., 2012). Finally, researchers have
demonstrated that hypothetical purchase tasks
may be regularly evaluated in single-subject
design to monitor treatment success (Bujarski,
MacKillop, & Ray, 2012; Madden & Kalman,
2010; McClure et al., 2013) and provide early
indicators of treatment failures (MacKillop &
Murphy, 2007) over treatment course periods.
If the TPT exhibits these clinical advantages in
dermatological study and use, health profes-
sionals may be able to use the TPT as a unique
behavioral screener to identify potential UVIT
abuse and monitor the emergence of depend-
ence. While these advantages to hypothetical
purchase tasks are dependent on evidenced
test–retest reliability—which is well-
documented in cigarette and alcohol purchase
tasks (e.g., Few et al., 2011; MacKillop et al.,
2008; Murphy, MacKillop, Skidmore, & Peder-
son, 2009)—we did not collect such measures;
as such, additional research on the TPT tem-
poral stability is needed.
The construct validity documented in this
study suggests that this TPT warrants further
investigation as both a research tool and diag-
nostic aid, preferably validated against more
objectively documented UVIT use. Two clini-
cal tools have recently been developed that
appear to be substantially more conservative in
assessing UVIT addiction: the Structured
Interview for Tanning Abuse and Dependence
(Heckman, Egleston, Wilson, & Ingersoll,
2008; Hillhouse et al., 2012) and the Tanning
Pathology Scale (Heckman et al., 2014; Hill-
house et al., 2010). We suggest future compar-
isons of TPT demand to scores on these two
scales, as well.
Afinal broader contribution of this study
lies in its public policy implications, specifically
in the P
max
variable to derive the tax price
associated with unit elasticity in participants’
demand curves. In doing so, P
max
provides an
empirical basis for determining excise taxes
Table 3
Correlation Matrix (Spearman’sρ) of Behavioral
Economic Demand Measures
Measure
Measure Q
0
P
max
O
max
EV
Q
0
P
max
.79
O
max
.84 .99
EV .84 .99 1.00
Note. All correlations were significant at the .05 alpha
level. See text for additional details.
DEREK D. REED et al.10
aimed at reducing problematic consumption
of unhealthy commodities, and likewise pre-
dicts potential revenues from said taxes via
computation of the O
max
variable (Hursh &
Roma, 2013; Roma et al., 2016). Behavioral
economists have used cigarette purchase tasks
to examine the potential policy implications of
tobacco (MacKillop et al., 2012), fuel (Reed,
Kaplan, Roma, & Hursh, 2014; Reed, Parting-
ton, Kaplan, Roma, & Hursh, 2013), or high-
caloric food (Epstein, Dearing, Roba, & Fin-
kelstein, 2010) taxation.
To date, we are unaware of any studies
examining the effects of the tanning tax levied
by the Patient Protection and Affordable Care
Act on UVIT demand. Examination of P
max
in
Panel D of Figure 2 suggests Recent tanners
would tolerate a tax of approximately $25 on a
$30 package; this constitutes an 83% tax that
far exceeds the current 10% tax levied by the
Patient Protection and Affordable Care Act.
Analysis of Non-Recent tanners suggests toler-
ance of an approximately $15 (50%) tax on
$30 tanning services. Interestingly, Never tan-
ners’point of tolerance with taxation approxi-
mates the 10% tax currently levied. These data
collectively indicate that the 10% tanning tax
falls in the inelastic portion of the demand
curve for consumers of tanning services, sug-
gesting that present policies are (1) likely to
be ineffective in reducing demand for those
individuals who currently engage in UVIT and
(2) limiting potential revenue that could be
used toward education and other abuse-
reduction initiatives. This 10% tax may, how-
ever, effectively discourage young adults from
initiating UVIT use. These behavioral eco-
nomic markers of taxation tolerance, coupled
with data indicating a relative lack of UVIT sal-
ons’compliance with the UVIT tax policy
(Jain, Rademaker, & Robinson, 2012), warrant
further investigation.
In summary, this study explored whether
behavioral economic measures of ultraviolet
indoor tanning (UVIT) demand via a novel
hypothetical tanning purchase task (TPT) are
associated with diagnostic criteria from estab-
lished measures of UVIT addiction (mCAGE
and mDSM-IV-TR) and self-reported frequency
of UVIT use.Although our findings suggest
the TPT has promise for assessing potential
abuse liability of UVIT for current UVIT users,
more psychometric work is necessary. Specifi-
cally, because this study utilized diagnostic
tools translated from addiction sciences
(i.e., behavioral economic demand), future
studies should compare the results of the TPT
with other emerging clinical tools explicitly
designed for UVIT addiction; particularly, the
Structured Interview for Tanning Abuse and
Dependence (Heckman et al., 2008; Hillhouse
et al., 2012) and the Tanning Pathology Scale
(Heckman et al., 2014; Hillhouse et al., 2010).
Further demonstration of the construct valid-
ity of the TPT may render it a beneficial tool
for academic, clinical, and public health policy
applications. In doing so, behavioral economic
markers of dependence may demonstrate fur-
ther translation to other behavioral addictions
not yet researched using demand measures by
applied behavioral economists.
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Received: February 6, 2016
Final Acceptance: June 22, 2016
DEREK D. REED et al.14
