Vol.5, No.10, 1667-1680 (2013) Health
http://dx.doi.org/10.4236/health.2013.510225
Utilization effects of Rx-OTC switches and
implications for future switches*
Chris Stomberg1, Tomas Philipson2, Margaret Albaugh1, Neeraj Sood3#
1Bates White, Washington DC, USA
2The Harris School, University of Chicago, Chicago, USA
3Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, USA;
#Corresponding Author:
Received 6 August 2013; revised 6 September 2013; accepted 24 September 2013
Copyright © 2013 Chris Stomberg et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
We examined the effect of over-the-counter
(OTC) conversion of prescription drugs on utili-
zation at the drug class lev el using monthly drug
utilization data from the US for the period 1999-
2010 for 9 drug classes: antihistamines, benign
prostatic hyperplasia medication, cholesterol
control drugs (statins), analgesics (triptans),
contraception medications (emergency contra-
ception), antiulcerants (proton pump inhibitors)
non-sedating antihistamines, weight-loss reme-
dies and erectile dysfunction remedies. We per-
formed interrupted time series analy sis to detect
a break in the trend of drug utilization following
OTC introduction. We found that the introduc-
tion of the first OTC drug increased drug utiliza-
tion at the class level by an average of 30% or
more. We concluded that OTC switches can be
an important policy tool for improving public
health in drug classes where a significant pro-
portion of the population is untreated and where
consumers can effectively manage treatment
with limited physician supervision.
Keyw ords: Over-the-Counter; Prescription Drugs;
Health Care Costs; Utilization; Spending
1. INTRODUCTION
The availability of over-the-counter (OTC) drug
products in the United States over the past 30 years has
been an integral part of a quiet revolution in health care.
Effective pharmaceutical treatments for a wide variety of
conditions have moved not just from lab to market, but in
a number of cases to store shelves where consumers have
ready access to them. In fact, according to Consumer
Healthcare Products Association (CHPA), “More than
700 OTC products on the market today use ingredients or
dosages available only by prescription less than 30 years
ago [1].” This has fundamentally changed how many
common health problems are treated. However, little is
known about how OTC conversion of pharmaceutical
products affects access to drugs and what impact it has
on health and health care costs.
Despite the OTC revolution, the majority of drugs
in the United States remain available only with a phy-
sician’s prescription. For some drugs, this barrier to
access has indisputable advantages. The prescrip-
tion-only status ensures physician’s supervision and
decreases the incidence of incorrect self-diagnosis and
potential misuse of drugs. However, on the other hand,
the monetary and time cost of physician visits required
for access to prescription drugs decreases access to
drugs. Thus, for some medicines, where consumers
can effectively manage treatment on their own, the
benefits of requiring a physician prescription might be
offset by the reduced access to drugs.
Unlike prescription drugs, OTC drugs are freely
available for consumers at pharmacies, supermarkets,
and other retail outlets. This ensures increased con-
venience and rapid access to effective medicine and
with appropriate labeling and consumer education,
this ease of access of OTC drugs can lead to increase
in use of drugs and better health. OTC drugs also keep
consumers engaged and allow them to take responsi-
bility for their own health. In addition, since OTC
drugs do not require a prescription, they could poten-
tially reduce time, travel and monetary costs related to
physician office visits. Although OTC drugs are
sometimes cheaper than their prescription-only coun-
terparts, they are typically not covered by insurance
[2]. Thus, the out of pocket price of the drug for the
*Funding source: This research was funded by a grant from Pfizer Inc.
The views expressed herein are those of the authors and do not neces-
sarily reflect the views of the funding source.

C. St omberg et al. / Health 5 (2013) 1667-1680
1668
consumer after the conversion can be higher. There-
fore, lifting a barrier to access, i.e. allowing a drug to
be purchased over the counter may or may not in-
crease drug utilization.
The association between financial barriers to access—
such as cost sharing features of prescription drug benefits
—and drug utilization is studied extensively (see to
Goldman, Joyce and Zheng (2007) for a review [3]), but
less is known about the impact of prescription status on
access and overall health.
Several papers have investigated the impact of OTC
conversion on utilization of a single molecule. Pierce and
Gilpin (2002) and Reed et al. (2005) examine the OTC
conversion of nicotine replacement therapy and find a
significant increase in utilization [4,5]. Moreau et al.
(2006) look at this question for emergency contraception
and find an increase in utilization of approximately 60%
following OTC conversion [6]. Sood et al. (2008) and
Sood et al. (2012) examined multiple molecules that
experienced a switch, both in the US and all over the
world [7,8]. They found mixed results on utilization. This
is not surprising as the effects of OTC conversion on uti-
lization might depend on the nature of treatment. For ex-
ample, whether the drug is intended to change behavior
(e.g smoking cessation medicines) or whether the drug is
intended to relive symptoms (e.g. medicines for prevent-
ing heartburn).
Other papers have shown that OTC conversion de-
creases prescription utilization of alternate therapies.
Gurwitz et al. (1995) investigates such effect on pre-
scription only vaginal antifungal medication and finds
that the number of prescriptions for alternate therapies
indeed decreased [9]. A similar question is studied in
Sullivan et al. (2005) and Furler et al. (2002) in the con-
text of the antihistamine class, in Filion et al. (2007) in
the context of the statin class, in Pierce and Gilpin (2002)
and Reed et al. (2005) in the context of nicotine re-
placement therapies and in Walker and Hinchliffe (2010)
for eye drops and eye ointment [4,5,10-13]. These au-
thors all find that the utilization of prescription only al-
ternative(s) decreases with the introduction of an OTC
drug. Essentially, all the studies above investigate the
degree of substitution between prescription-only and
OTC drugs. Thus, it is unclear from prior studies whether
OTC conversion increases access to drugs, that is,
whether increase in use of OTC drugs is more than offset
by decrease in use of prescription-only substitute treat-
ments.
In this study we add to this literature by estimating the
effects of OTC conversion on changes in drug utilization
at the drug class level. In particular, we are interested in
measuring the effects of OTC conversion on use of the
OTC drug itself as well as that of the prescription-only
alternative(s). Once a drug becomes available over the
counter, it is more accessible than other similar therapies
requiring prescription. Thus, a straightforward conse-
quence of an OTC switch is a simple substitution from
the restricted drugs to the over-the-counter alternative, i.e.
an increase in utilization of the OTC drug at the expense
of the other drugs in the same drug class. A drop in the
number of prescriptions after an OTC conversion is
well-documented in several studies (see references
above). By contrast, we estimate the effect of an OTC
switch on utilization beyond this simple substitution ef-
fect. We intend to study the extent to which removing a
regulatory barrier contributes to the more widespread use
of a therapy class. To our best knowledge, this is the first
study to investigate the impacts of a prescription status
change for the entire drug class—including both OTC
and prescription only alternatives.
In addition to reviewing the impact of OTC conver-
sion on utilization, we then take the projected utilization
increase and discuss possible implications for health and
cost outcomes for potential future OTC switch candi-
dates. Prior studies document significant public health
and quality of life impacts of OTC medications in drug
classes such as allergy medications and smoking cessa-
tion products. For example, Sullivan et al. (2003) simu-
late the health effects of OTC non-sedating antihista-
mines and predict a significant level of annual savings in
time-discounted, quality-adjusted life years due to de-
creasing risks of injuries and fatalities associated with
sedation [14]. Studying the class of nicotine replacement
therapies (NRT), Reed et al. (2005) document an in-
crease in the proportion of smokers using NRT as well as
in the reported abstinence immediately after the OTC
switch in 1996 [5]. Using data from randomized trials,
Stead et al. (2008) confirm the result that NRT products
increase the chance of successful cessation [15]. Finally,
several studies find that easier access to emergency con-
traception increases utilization but has no effect on the
pregnancy rates (see meta-studies conducted by Ray-
mond et al. (2007) and Polis et al. (2007)) [16,17]. In
summary, increased availability of drugs in an OTC set-
ting can lead to significant increase in access to drug
with a potential for significant public health benefits.
This article proceeds as follows: Sections 2 and 3
describe our data and methods, respectively. Section 4
presents the results of our empirical estimation. We
conclude in Section 5 with a discussion of our findings,
which we relate to potential health outcomes and cost
impacts.
2. DATA
We studied the effect of moving a drug to OTC status
on total utilization of the relevant drug class. Our data
are from the MIDAS database maintained by IMS Health

C. St omberg et al. / Health 5 (2013) 1667-1680 1669
Inc. The database provides us with monthly drug
utilization data in the US through multiple channels, such
as retail pharmacies, hospitals, and long-term care insti-
tutions. We focus on drug usage in nine different drug
classes. We describe those classes below. The period for
our study is years 1999 to 2010, the number of obser-
vation is 1287.
OTC drugs are available at multiple retail outlets such
as pharmacies, grocery stores, and convenience stores.
Unfortunately, the MIDAS database only includes phar-
macies as retail points and thus the total usage of OTC
drugs is undercounted. We applied two methods to adjust
the MIDAS data. First, we used IRI data to obtain yearly
drug sales at all point of sale and compared that to total
yearly sales calculated from the MIDAS dataset. IRI
collects scanner data from retail outlets. We used the
ratio of those two as a multiplier for adjusting monthly
MIDAS data. Our method is valid if we can reasonably
assume that the MIDAS-IRI sales ratio is stable over the
course of a year. Second, in certain cases annual 10 K
reports to shareholders contain drug level sales informa-
tion. This information is considered to be more accurate
than point-of-sale survey data. We used the ratio of
yearly sales calculated from the MIDAS database and
yearly sales extracted from the 10 K forms to carry out
an adjustment similar to the one before. Whenever it was
possible we constructed our data in both ways and tested
for robustness of our result.
For the project, we considered drug classes that either
experienced an OTC status switch during our period of
study or are possible candidates for such switch in the
near future. Ultimately, we have included nine drug
classes in our study:
● Benign prostatic hyperplasia medication: alpha bloc-
kers (BPH)
● Statins
● Triptans
● Emergency contraception (plan-b)
● Proton pump inhibitors (PPIs)
● Non-sedating antihistamines
● Weight loss remedies—excluding herbal products
● Erectile dysfunction—excluding herbal products
● Urinary incontinence or overactive bladder drugs
(OAB)
During our period of study, four of these drug classes
experienced at least one OTC status switch. Two of these
drug classes (antihistamines and PPIs) experienced two
OTC status switches. The other two drug classes (emer-
gency contraception and weight loss) saw only one
molecule switch to OTC status. In the other five drug
classes all molecules remained prescription only during
the period covered by this study: 1999-2010. Table 1
shows the list of molecules considered for each of the
classes and provides information on the date of the OTC
switch(es) for each class.
Figures 1-4 show monthly utilization for the classes
and select molecules that experienced an OTC switch. To
aid interpretation the time of the OTC switch (green line)
as well as generic entry (red line) is marked on the
graphs. One can see, for example, the timing of the two
OTC switches occurring in the antihistamine and PPI
classes during the study period. All of the molecules with
an OTC switch exhibit a clear break in the time trend. In
some examples the pattern change is complex. At the
Table 1. Drug classes in the study (switch during study period).
Class Molecule OTC switch datea
Non-sedating Antihistamines Loratadine Dec-02
Cetirizine Jan-08
Desloratadine, Exofenadine, Levocetirizine
Emergency contraception Levonorgestrel Jul-06
PPI Omeprazole Sep-03
Lansoprazole Nov-09
Esomeprazole, Pantoprazole, Rabeprazole, Dexlansoprazole
Weight loss Orlistat May-07
Amfepramone, Benzphetamine, Fenfluramine, Mazindol, Methamphetamine,
Phendimetrazine, Phentermine, Sibutramine
BPH Doxazosin, Terazosin, Alfuzosin, Tamsulosin, Silodosin
Triptans Almotriptan, Eletriptan, Naratriptan, Rizatriptan, Sumatriptan, Zolmitriptan, Frovatriptan
ED Alprostadil, Sildenafil, Tadalafil, Vardenafil
Statins Atorvastatin, Cerivastatin, Fluvastatin, Lovastatin, Pitavastatin, Pravastatin, Rosuvastatin,
Simvastatin
OAB Darifenacin, Fesoterodine, Flavoxate, Oxybutynin, Solifenacin, Tolterodine, Trospium
aOTC
switch dates indicate the date that OTC sales first appear in the data.

C. St omberg et al. / Health 5 (2013) 1667-1680
1670
(a)
(b)
Figure 1. Effect of OTC switch and generic entry on antihista-
mine utilization. (a) Antihistamine class; (b) Loratadine mole-
cule.
class level, changes are apparent but somewhat attenu-
ated—which is to be expected as the switching mole-
cules often represent a fraction of total class utilization.
The differences between the observable molecule and
class-level effects, however, are not fully explained by
market share of the switching drug, which suggests that
there may also be within-class substitution effects.
3. METHOD
3.1. Overview
Although primary interest is directed toward class-
level utilization effects of OTC switches as this has the
greatest policy implications, our empirical approach also
addresses the effect of OTC switch on utilization for the
molecule. By doing this, we are able to shed some light
on the effect of a switched drug’s market share on its
class-wide impact as well as potential substitution effects
that may occur among drugs within a class.
Our approach focuses mainly on identifying a break in
Figure 2. Effect of OTC switch and generic entry on emer-
gency contraception utilization. Note: Levonorgestrel was the
only drug in emergency contraception class drug class.
the pattern of utilization for the molecule or class post
OTC switch without necessarily benchmarking directly
against other classes or molecules, i.e. we use an inter-
rupted time-series approach. The essence of this ap-
proach is to build a model that predicts utilization with-
out accounting for the OTC switch, and then to compare
that model against an alternative model that takes the
timing of the OTC switch into account. The difference
between these models can be used to estimate the sig-
nificance and magnitude of the OTC effect. We also ex-
plore a variant based on a predictive approach that com-
pares actual post-OTC switch utilization to forecasts
from a model fit to pre-switch data. These designs are
quite flexible, and encompass a wide variety of potential
specifications for the baseline and comparison models.
A difference-in-difference method is not employed due
to the difficulties in finding appropriate benchmarks. For
example, utilization of other molecules within a class
may change due to an OTC switch in their class and thus
create a moving target for comparison. Similarly, bench-
marks drawn from other product classes or countries are
likely confounded by differences such as product de-
mand, lifecycle status, and regulatory/reimbursement en-
vironment.
3.2. The Models
The plots in Figures 1-4 illustrate the complexity of
dynamics that can occur both before and after OTC
switch. This complexity underscores several important
modeling considerations:
● Product life-cycle is important to take into account as
products can have accelerating, decelerating, or de-
clining growth depending on where in the life-cycle
they are at the time of OTC switch.
● Utilization dynamics can change in a number of ways
(e.g., intercept, slope, curvature) after OTC switch.

C. St omberg et al. / Health 5 (2013) 1667-1680 1671
(a)
(b)
Figure 3. Effect of OTC switch and generic entry on PPI utili-
zation. (a) PPI drug class; (b) Omeprazole.
● Generic switches and OTC switches elsewhere within
class can be a significant confounding factor for es-
timating the effect of OTC switches [8].
● A significant amount of month to month variation in
utilization can be driven by seasonal effects (depend-
ing on the molecule); handling this should improve
precision.
Taking the above considerations into account, we es-
timate a range of models that allow varying levels of
flexibility in the comparison. As a baseline, we estimate
univariate models at the class and molecule levels. These
are described in Table 2.
In these models, we employ a linear trend with sea-
sonal effects as the benchmark. For analytical conven-
ience we examine changes to the logarithm of quantity
utilized. To account for the possible non-linear effects of
lifecycle (i.e. changing underlying processes) we limit
estimation and comparison to three years of data pre/post
OTC switch where underlying trends in the utilization of
drugs are roughly linear (see Figures 1-4). In other
words, focusing on the three years pre and post launch
(a)
(b)
Figure 4. Effect of OTC switch and generic entry on weight
loss drug utilization. (a) Weight loss drug class; (b) Orlistat.
helps to account for typical life-cycle patterns by miti-
gating the effect of strong early growth periods on the
estimate. Alternatives to this are explored in our sensitiv-
ity analyses.
The four parameters q
ij

are quarterly intercepts that
allow for the possibility of seasonal variation. The vari-
able t is a trend variable that is equal to one in the first
period available in the data and increases by one unit
each month. The variable is an indicator vari-
able equal to one if a branded OTC version of the drug is
available, and zero otherwise. For example, the first
model effectively embeds two models (setting aside sea-
sonal effects):
OTC ijt
● Prior to OTC switch
(OTC 0
ijt

):


ln ijt ijij ijt qt 


● After OTC switch
(OTC 1
ijt

):

 
ln ijtij ijijijt qt  


The parameter ij

in this model therefore estimates
the amount of shift in the intercept that occurs after OTC
switch for molecule j. Since this model is estimated us-
ing logarithms, the parameter estimate ij

is approxi-

C. St omberg et al. / Health 5 (2013) 1667-1680
1672
Table 2. Baseline model specifications (molecule and class).
Model Description Equation
Dummy variable
● Linear trend
● OTC effect: intercept only
● 3 yr. estimation window

4
1
ln OTC
q
ijtijijijt ijijt
q
qt  


 


4
1
ln OTC
q
jtjjjt jijt
q
Qt  


 

Predictive
● Linear trend
● Lagged dependent variable
● 3 yr. estimation window

4
1
1
ln q
ijtijij ijtijijt
q
qq 


t

 


4
1
1
ln q
jtjj jtjijt
q
QQ  


t

 

4.1. Class Results
mately equal to the percentage change in qijt post OTC
switch for small changes. In other words, this parameter
is of direct interest to us because it captures an estimate
of the OTC switch effect in percentage terms.
The estimation results for these regression models are
summarized in Table 3. Recalling that the coefficient
estimates for the OTC variables in the dummy-variable
models can be interpreted approximately as the percent-
age change in utilization associated with OTC switch, the
first column of estimates in Table 3 indicate a 13% in-
crease for Antihistamines, a 40% increase for emergency
contraception, and an 88% increase for Weight Loss. The
estimated effect for PPIs, however is slightly negative
but not significant. The second column in this table
shows the results of a pooled model that averages the
OTC effect across all switching classes. This model es-
timates a roughly 25% average effect of OTC switch on
utilization. These effects are all significant at the 1%
level.
A panel model approach was used to extend the uni-
variate models to account for changes in other factors
that might be coincident with the OTC event, e.g. generic
entry. These models employ data across all drug classes
in our study—including those that did not experience an
OTC switch (but which might have experienced generic
entry, for example). We also examine a fully pooled
model using the panel data. This identifies an average
magnitude of the OTC switch effect across the classes
included in our study.
4. RESULTS
The third and fourth columns of Table 3 illustrate the
effect of accounting for the estimated effects of generic
competition and secondary OTC switches in the baseline
models. The overall results are qualitatively similar to
the initial estimates: the pooled average OTC switch ef-
fect rises to 27% after accounting for these other factors.
It is interesting to note that our estimates indicate that
generic entry is associated with a 13% to 16% decline in
overall utilization. This is somewhat balanced by rela-
tively small estimated increases in utilization as the
number of generic competitors increases (1% to 1.6% per
competitor). A small negative effect is also associated
second OTC launch. These effects may be capturing the
“aging” of a drug class—i.e. classes experiencing abso-
lute decline in utilization due to entry of newer classes,
or the effects of reduced marketing efforts by brands
[18].
We find that the presence of an OTC product generally
has an overall positive and economically meaningful
influence on class utilization. For the baseline models
employing class-level data we find an overall positive
effect of between 25% and 42% on utilization depending
on the model used and the method of measurement. We
also find, however, that the magnitude of this effect var-
ies significantly across switching classes—ranging from
less than 10%, to over 140%.
As expected, we find generally more pronounced
utilization effects in the presence of an OTC product at
the molecule level. The estimated OTC effect averaged
across switching molecules ranges from 29% to 167%
depending on model and measurement method. This
likely reflects a combination of market share and substi-
tution effects. In some classes, e.g. antihistamines, there
is notable evidence of substitution effects at OTC switch,
while in others, e.g. weight loss, the class-expanding
effect of the OTC switch dominates. There is also con-
siderable variation in the estimated OTC effect from
molecule to molecule.
We turn now to an alternative method for evaluating
the estimated utilization changes due to OTC switch
based on comparing model forecasts (without the OTC
effect) to actual utilization. The result of this alternative
method is summarized in Table 4. Using this methodol-
ogy we find that the relative ordering of the effects
across the four switched classes remains unchanged.
Nevertheless, we do find noticeable differences com-
pared to estimates based on the model parameter esti-
The results from both class- and molecule-level mo-
dels suggest that extrapolating the potential effects of
future OTC switches could depend on the selection of a
historical analog.

C. St omberg et al. / Health 5 (2013) 1667-1680 1673
Table 3. Class baseline models—selected parameter estimates.
Linear trend Linear trend with generic and 2nd OTC
Variable
By class Pooled By class Pooled
OTC Antihistamines b 0.133 0.076
t-stat. 4.298 2.261
p-value 0.000 0.024
OTC Emergency Contraceptive b 0.398 0.334
t-stat. 3.993 3.292
p-value 0.000 0.001
OTC PPIs b −0.020 0.033
t-stat. −1.091 0.865
p-value 0.275 0.387
OTC Weight Loss b 0.877 0.877
t-stat. 6.451 6.442
p-value 0.000 0.000
OTC Pooled (average) b 0.254 0.270
t-stat. 7.030 6.478
p-value 0.000 0.000
Second OTC Switch b −0.066 0.027
t-stat. −3.163 0.988
p-value 0.002 0.323
Generic Entry b −0.126 −0.160
t-stat. −3.958 −5.808
p-value 0.000 0.000
Number of Generic competitors b 0.010 0.016
t-stat. 2.290 4.042
p-value 0.022 0.000
R2 0.9988 0.9985 0.9988 0.9986
N 1138 1138 1138 1138
Table 4. Class baseline models—predicted vs. actual utilization.
Cumulative percentage growth over post-switch period
Class Model
6 mo. 12 mo. 24 mo. 36 mo.
Antihistamines 11.5% 10.4% 6.2% 3.5%
Linear trend 12.5% 11.7% 7.7% 4.6%
Linear trend with generic and 2nd OTC 10.5% 9.2% 4.7% 2.5%
Emergency contraception 5.4% 24.4% 34.2% 31.8%
Linear trend 5.4% 24.4% 34.2% 31.8%
Linear trend with generic and 2nd OTC 5.4% 24.4% 34.2% 31.8%
PPIs 2.3% −1.1% −5.2% −8.0%
Linear trend −0.3% −3.4% −6.6% −8.5%
Linear trend with generic and 2nd OTC 4.9% 1.2% −3.8% −7.5%
Weight Loss 176.8% 141.3% 139.0% 139.3%
Linear trend 176.8% 141.3% 139.0% 139.3%
Linear trend with generic and 2nd OTC 176.8% 141.3% 139.0% 139.3%
Average 49.0% 43.8% 43.5% 41.7%

C. St omberg et al. / Health 5 (2013) 1667-1680
1674
mates. For example, the estimated three-year cumulative
percentage change for the Antihistamine class ranges
from 3% to 5% when estimated via this method versus
8% to 13% based on examination of parameter estimates.
The OTC effect for Weight Loss moves from 88% to
close to 139%. Overall, the average 3-year OTC switch
effect on class-level utilization computed via this method
is about 42%.
4.2. Molecule Results
The estimation results for these regression models are
summarized in Table 5. Once again we find that the es-
timated effects of OTC switch vary considerably from
molecule to molecule, ranging from apparently negative
estimated effects on utilization for the omeprazole switch,
to 212% for Orlistat. Pooling across the molecules that
switch we find an average utilization change of 29% in
the presence of an OTC switch if generic entry and other
OTC switches are not accounted for, and 46% if they are.
The estimated effect of generic entry is supported in
part by events that occur outside the switching molecules.
As a result, it is possible to separate the estimated effect
of generic entry within molecule from that of other
molecules within class. Both effects are significant and
have opposing signs suggesting entry of a generic within
the molecule is associated with a drop in utilization of
the molecule while generic entry elsewhere in the class is
associated with the opposite effect. This may be captur-
ing the effects of follow-on product introductions.
In Table 6, we compute average percentage changes
in utilization in the presence of an OTC switch based on
a comparison of forecast utilization (without OTC effect)
to actual utilization. As with the class models, the esti-
mated 36 month changes are of a magnitude comparable
to those derived from model parameter estimates in the
individual molecules. The main deviation from this gen-
eral rule is Orlistat, where the OTC effect is estimated to
be over 750% as opposed to 212%. The larger estimate
appears more realistic upon inspection of Figure 4. In
2006, the last full year before OTC switch, average
monthly utilization was approximately 0.9 million units.
By 2008, the first full year after OTC switch, utilization
of Orlistat had jump to 5.75 million units—a 540% jump
from 2006. This does not take into account the large
spike in utilization in 2007 and the general downward
trend in pre-OTC utilization, both of which are captured
by the models. The overall average percentage change
estimate (167%) is dominated by the Orlistat result.
Comparison of these molecule-level results with the
overall class results suggests the presence of substitution
effects. For example, the loratidine (Claritin) OTC
switch appears to have resulted in a 40% increase in
loratidine utilization. However, class-wide utilization of
non-sedating antihistamines changed only marginally
post Claritin switch—perhaps between 3% and 5%.
Claritin market share was roughly 30% at this time, so
less of a change in class-level utilization occurred than
would be predicted if other products in class were unaf-
fected by the Claritin OTC switch (i.e. 30% × 40% =
12%). This suggests that other molecules in this class
were treated as substitutes by some patients and saw de-
clines after Claritin’s switch. In other cases, such as
weight loss, however, the OTC switch is clearly class-
expanding.
4.3. Sensitivity Analyses
It is important to recognize that the quality of the
overall estimate of the OTC effect can be affected by the
quality of the estimated models. If the explanatory vari-
ables do not properly account for important factors that
affect utilization, then the estimation may be confounded
by omitted variables, or lead to omitted variables bias.
Similarly, functional form misspecification of our esti-
mates could also lead to improper inference. To validate
our results, we thus estimate a variety of alternative
specifications and examine the sensitivity of our results
to these alternative specifications.
The alternatives we explored are summarized in Table
7. In particular, we considered the following alternatives
to the baseline models:
● Change in trend: Allow slope of trend to change
post OTC switch
● Flexible OTC dummies: Estimate 4 separate, non-
overlapping, post OTC switch dummies (0 - 6 m., 6 -
12 m., 12 - 24 m., 24+ m.)
● Alternative life-cycle model: Estimate quadratic
trend and use full pre-OTC switch period
● Predictive model: Estimate forecast model over pre
OTC-switch data only, compare forecasts to actual
utilization
We generally find that the baseline models deliver ro-
bust estimates. For example, in both Weight Loss and
Emergency Contraception classes (and molecules), vir-
tually all specifications identify large, significant, and
persistent positive impacts on utilization attributable to
OTC switch.
The main exception to this rule is omeprazole where
estimates of the OTC effect vary wildly across alterna-
tive specifications—switching signs and changing levels
of significance. Omeprazole has an unusually large up-
swing in utilization driven largely by generic utilization
post OTC switch (and generic launch). This is likely
driven by formulary incentives put in place by payors
after Prilosec lost exclusivity (and an OTC became
available). In this case, it is not clear that generic and
OTC effects are separately identified at the molecule
level.

C. St omberg et al. / Health 5 (2013) 1667-1680 1675
Table 5. Molecule baseline models—selected parameter estimates.
Vari ab le By class Pooled
Class
Linear
trend
Linear trend with generic
and other OTC
Linear
trend
Linear trend with generic
and other OTC
Antihistamines OTC Cetirizine b 0.2990.898
t-stat. 5.1006.399
p-value 0.0000.000
OTC Loratadine b 0.2970.390
t-stat. 3.7064.978
p-value 0.0000.000
Emergency OTC Levonorgestrel b 0.3980.246
Contraception t-stat. 3.9782.462
p-value 0.0000.014
PPIs OTC Omeprazole b −0.674 −0.512
t-stat. −8.993 −8.110
p-value 0.0000.000
OTC Lansoprazole b 0.1280.942
t-stat. 2.51011.123
p-value 0.0120.000
Weight Loss OTC Orlistat b 2.1192.119
t-stat. 8.0468.044
p-value 0.0000.000
OTC Pooled (average) b 0.2880.461
t-stat. 4.4196.900
p-value 0.0000.000
OTC Other in class b 0.116 0.090
t-stat. 1.611 1.277
p-value 0.107 0.202
Generic entry b 0.422 0.389
t-stat. −10.388 −10.423
p-value 0.000 0.000
Generic entry other in class b −0.699 −0.683
t-stat. 6.794 6.334
p-value 0.000 0.000
5. DISCUSSION
We examined historical OTC conversion and found
significant increase in drug utilization at the class level
for most drug classes analyzed. These results suggest that
OTC conversion increases access to drugs rather than
just causing substitution between OTC and prescription
drugs. Increased access to drugs in these drug classes due
to OTC switches might be related to better health out-
comes to the extent that consumers can use drugs appro-
priately with limited physician supervision. Several pa-
pers suggest that consumers have the ability to make
reasonable choices in an OTC setting. For example,
Brass (2004) examines data on consumer behavior from
a simulated OTC setting for more than 3000 patients.
They find that most study participants appropriately se-
lected cholesterol lowering statin therapy and success-
fully managed the treatment [19]. A paper by Melin et al.
(2004) and a later study by Brass et al. (2008) both con-
firm this conclusion [20,21].
Industry reports indicate that several drug classes in-
cluding cholesterol, benign prostatic hyperplasia (BPH),
erectile dysfunction, incontinence, migraine, and sleep-
ing aids might experience OTC switches in the near fu-
ture [22]. Our finding of positive results on utilization on
a class wide basis suggests that these future Rx-OTC
switches might confer health benefits.

C. St omberg et al. / Health 5 (2013) 1667-1680
1676
Table 6. Class baseline models—predicted vs. actual utilization.
Cumulative percentage growth over post-switch period
Class Model
6 mo. 12 mo. 24 mo. 36 mo.
Antihistamines Loratadine 11.7% 23.9% 33.9% 41.5%
Linear trend 8.9% 20.0% 28.6% 37.9%
Linear trend with generic and 2nd OTC 14.4% 27.7% 39.2% 45.2%
Cetirizine 52.3% 64.2% 84.5% 89.2%
Linear trend 42.1% 35.2% 37.9% 36.5%
Linear trend with generic and 2nd OTC 62.4% 93.1% 131.1% 142.0%
Emergency Contraception Levonorgestrel 5.4% 24.4% 34.2% 31.8%
Linear trend 5.4% 24.4% 34.2% 31.8%
Linear trend with generic and 2nd OTC 5.4% 24.4% 34.2% 31.8%
PPIs Omeprazole −19.4% −21.8% −18.7% −8.4%
Linear trend −23.0% −25.0% −20.8% −9.5%
Linear trend with generic and 2nd OTC −15.8% −18.7% −16.6% −7.3%
Lansoprazole 83.7% 87.3% 87.1% 87.1%
Linear trend 12.4% 14.6% 14.5% 14.5%
Linear trend with generic and 2nd OTC 155.0% 160.0% 159.7% 159.7%
Weight Loss Orlistat 1058.2%826.9% 786.3% 760.9%
Linear trend 1058.2%826.9% 786.3% 760.9%
Linear trend with generic and 2nd OTC 1058.2%826.9% 786.3% 760.9%
Average 198.6%167.5% 167.9% 167.0%
Table 7. Alternative model specifications (molecule and class).
Model Description Equation
● Linear trend
● OTC effect: intercept and slope
● 3 yr. estimation window

4
1
lnOTC OTC
qo
ijtijijijtijijijtijt
q
qt  

t

  


4
1
lnOTC OTC
qo
jtjjjtjjjt ijt
q
Qt 

t

  

● Linear trend
● OTC effect: 4 separate intercept shifts
(0 - 6 m., 6 - 12 m., 12 - 24 m., 24+ m.)
● 3 yr. estimation window
 
44
11
ln OTC
qss
ijtijijijt ijijt
qs
qt  


 
 
 
44
11
ln OTC
qss
jtjjjt jijt
qs
Qt  


 
 
Dummy
variable
● Quadratic trend
● OTC effect: intercept only
● Full estimation window

4
2
1
ln OTC
qlq
ijtijijijt ijijijt
q
qt  

t

 


4
2
1
ln OTC
qlq
jtjjjt jjijt
q
Qt 

t

 

Predictive
● Quadratic trend
● Lagged dependent variable
● Full estimation window

4
2
1
1
ln qlq
ijtijij ijtijijijt
q
qqt  


t

 


4
2
1
1
ln qlq
jtjj jtjjijt
q
QQt  


t

 

Given the potential significance of some of these fu-
ture changes, it is worthwhile to walk through some of
the prospective switch markets in greater detail. We dis-
cuss statins, OAB treatment, migraine, and ED drugs in
greater detail. Each of these markets has its own set of
unique issues that highlight the potential benefits from
increased access to OTC medications.
A potential market for future switch is statins. Several
statins have recently applied for OTC status. Although
these applications have so far been denied by the FDA,
the discussion is likely to continue. Brass et al. (2006)
calculates the direct effect of an OTC conversion in the
statin class and finds that 23,000 to 33,000 coronary
heart disease events can be prevented per million OTC

C. St omberg et al. / Health 5 (2013) 1667-1680 1677
statin users [23]. Juxtaposing this result with the fact that
a large proportion of adults with high levels of choles-
terol remain untreated suggests that increased access to
statins in an OTC setting can have significant public
health benefits. Using our results of a 30% average mar-
ket increase and an estimate of 36.5 million statin users
calculated from NHANES (2007-2008) a nationally rep-
resentative sample, even the conservative estimate of
23,000 averted heart disease events per million statin
users from Brass et al. (2006) could result in as many as
250,000 averted events due to increased utilization fol-
lowing OTC conversion of statins.
When measuring the cost savings from the availability
of OTC statins, one of the biggest savings follows di-
rectly from averted events. Cardiac events cost approxi-
mately $35,000 to treat [24]. This results in a savings of
over $8 billion dollars over a ten year period due to
averted events alone. Data on the source of payments
demonstrate that these savings would accrue primarily to
private insurers and to Medicare, each of whom currently
pay for approximately 35% of costs related to MCE
events. Insurers, including Medicare, would also save on
drug costs. New users would presumably buy through the
OTC channel, which is typically not covered by the in-
surer. Additionally, some previous prescription users
would likely switch to purchasing statins OTC, relieving
the insurer of existing costs. New consumers of statins
would incur the increased cost of purchasing the statin
OTC.
While statins have a significant impact on expected
mortality and heart events, many of the other prospective
OTC drug classes have a significant impact on health
related quality of life. For over active bladder (OAB)
drugs, availability to consumers without requiring a doc-
tor visit may be particularly beneficial in improving
health related quality of life. An estimated 50% to 70%
of women with urinary incontinence fail to seek medical
evaluation because of social stigma. Access to effective
drugs in an OTC setting could result in a significant in-
crease in use of these drugs and a significant improve-
ment in quality of life for women with urinary inconti-
nence. Again, using our estimate of increased class wide
drug utilization and the current population of OAB users
of approximately 4.2 million and the increase in QALY
score of 0.04 per year from Cardozo et al. (2010), we
estimate an overall QALY gain of over 50,000 per year
[25].
For sufferers of OAB, of primary concern is the ability
to attend work and function in the work environment.
According to Clemens (2011), which uses results of a
self-administered, Internet-based questionnaire, the Na-
tional Health & Wellness Survey, work productivity is
defined as the sum of absenteeism and presenteeism [26].
The study reports the average number of days missed
from work in the last 3 months (absenteeism) and the
average number of days in the last 3 months where pro-
ductivity at work was reduced by half or more (presen-
teeism) for both Rx treated OAB sufferers and untreated
OAB sufferers separately.
The differences between productivity figures for
treated and untreated populations can be used to measure
the increased productivity gained when using OAB
medications. Treatment decreases both absenteeism,
which results in a 2.38% productivity gain, and presen-
teeism, which results in a 1.79% productivity gain. These
imply work productivity gains of $1,513/year for women
and $1965/year for men when we use $36,278 as the
average earnings of women and $47,127 as the average
earnings of men with labor market participation rates of
60% for women and 73% for men [27]. Because most of
the new users of OTC OAB medication would be women,
taking the straight average of the productivity gains and
assuming that gain for each new user results in over $2
billion in productivity gains per year.
More than 1 in 10 people in the US suffer from mi-
graines, with the highest prevalence among women and
working age adults [28]. Migraine attacks not only in-
crease health care costs but also severely impact quality
of life and productivity at work and home. In fact, indi-
rect costs due to absenteeism and lost productivity ac-
count for the majority of societal burden of migraine
costing US employers about $13 billion (in 1999 dollars)
a year [29]. These large costs of migraine are primarily
due to under treatment with the majority of migraine
sufferers reporting that they had not seen a doctor within
the last one year [30]. Roughly half of migraine sufferers
report using OTC medications only. However, current
OTC choices for treating migraine in the US are limited
as the newer and more efficacious treatments (triptans)
are currently available by prescription only. In 2006,
sumitriptan—the first triptan, introduced in 1992—be-
came available as an OTC drug in the UK and Germany.
Similar OTC conversion in the US has the potential to
significantly help employers by lowering health care
costs, reducing absenteeism and improving productivity.
For example, previous research suggests that roughly 6
million working age men and 18 million working age
women suffer from migraines. Among migraine suffer-
ers’ men required 3.8 days of bed rest and women re-
quired 5.6 days of bed rest. If OTC availability of trip-
tans provides effective migraine treatment for roughly 1
in 6 migraine sufferers, then it would reduce the number
of bed days by 3.8 million for men and 16.8 million for
women. This reduction in bed days valued at average
wages by gender would yield savings to society of
roughly 3.5 billion dollars. OTC availability will also
save health care costs by reducing costs related to physi-
cian visits which account for 60% of the direct costs of

C. St omberg et al. / Health 5 (2013) 1667-1680
1678
migraines [29]. Finally, OTC sumitriptan would also
likely result in lower prescription drug costs for employ-
ers as it would cause substitution from branded prescrip-
tion triptans to sumitriptan which is available as a ge-
neric in the US since 2009.
The ED market includes a significant grey market.
Many ED sufferers turn to online pharmacies or to non-
prescription alternatives, such as herbal remedies. Camp-
bell et al. (2012) examined Viagra that was purchased
through internet pharmacies and found that more than
75% were counterfeit [31]. While counterfeit, they did
contain sildenafil citrate, but not in the dose indicated on
the label. Additionally, they were frequently shipped
without the corresponding safety information, including
the contraindication for nitrates. Campbell et al. (2012)
also found that many herbal remedies contain sildenafil
or similar synthetic substances without listing them on
the label. Even within products, there was significant
variation in the level the prescription molecule. We be-
lieve it is likely that many of these grey market users
would switch to the OTC, were it available. This could
have important public health benefits by switching con-
sumers into a product with controlled and regulated
manufacturer process and quality control.
This robust grey market also indicates that the pre-
scription market is not filling consumer demand. This
may be driven by embarrassment when discussing medi-
cation with doctors. Alternately, costs may be a factor for
consumers. ED drugs are often not covered by insurance
companies or have strict quantity limits imposed for
coverage. Increased availability of drugs to treat erectile
dysfunction in an OTC setting could result in significant
improvement in quality of life for men who suffer from
erectile dysfunction. Our models suggest OTC availabil-
ity could result in approximately 2.5 million new users (a
30% increase from the over 8 million users of prescrip-
tion and grey market ED drugs) [32]. Prior studies show
that prescription ED drugs improve QALYs by 0.35 per
user, for a total QALY gain of over 500,000 [33].
While we have suggested the possible magnitude of
health effects for future OTC switches, it is important to
note that future research will need to address this ques-
tion more thoroughly. In addition to estimating the health
improvements for new users of the drug class, the poten-
tial decrease in health for users switching from a physi-
cian monitored environment to an OTC setting must also
be considered. Additionally, OTC drugs may be the
lowest available dose, causing some prescription switch-
ers to utilize an inadequate dose of the drug. Finally,
some new users of the drug may not actually receive any
benefit from it while others will suffer side effects.
We discussed potential health benefits of an Rx-to-
OTC switch. However, to fully understand the impact of
an OTC conversion, it is essential to also learn about the
consequent changes in health care costs, such as expen-
ditures on drugs and physician’s services. Un-ortunately,
the literature on cost consequences is sparse. Several
studies use administrative claim data and conclude that
OTC conversion reduces insurers’ total spending on
drugs and medical services (see Sood et al. (2008) for a
literature review) [7]. However, the extent to which
lower spending by insurers is offset by increased con-
sumer expenditure is not known. A few papers attempt to
estimate social costs using decision-analytic methods and
conclude that there are potential cost savings related to
OTC introduction (for example Sullivan et al. (2003))
[14]. Some studies also estimate the effect of OTC
switches on physician spending with mixed findings. A
decrease in office visits is documented after the introduc-
tion of OTC vaginal antifungal (see Gurwitz et al. (1995))
while no change was found related to the introduction of
OTC H2RAs (see Shaw et al. (2001)) [9,34]. Overall,
future research should consistently account for the cost
effects of an Rx-to-OTC switch for different cost catego-
ries and for different payers in the health care market:
consumers, insurers and the government.
REFERENCES
[1] Consumer Healthcare Products Association (2012) Rx-to-
OTC Switch.
http://www.chpa-info.org/scienceregulatory/Switch_SR.a
spx
[2] Freudenheim, M. (2003) Claritin’s price falls, but drug
costs more. The New York Times.
[3] Goldman, D.P., Joyce, G.F., et al. (2007) Prescription
drug cost sharing: Associations with medication and
medical utilization and spending and health. JAMA, 298,
61-69.
[4] Pierce, J.P. and Gilpin, E.A. (2002) Impact of over-the-
counter sales on effectiveness of pharmaceutical aids for
smoking. JAMA, 288, 1260-1264.
[5] Reed, M.B., et al. (2005) The effect of over-the-counter
sales of the nicotine patch and nicotine gum on smoking
cessation in California. Cancer Epidemiology, Biomar-
kers & Prevention, 14, 2131-2136.
http://dx.doi.org/10.1158/1055-9965.EPI-04-0919
[6] Moreau, C., Bajos, N. and Trussell, J. (2006). The impact
of pharmacy access to emergency contraceptive pills in
France. Contraception, 73, 602-608.
http://dx.doi.org/10.1016/j.contraception.2006.01.012
[7] Sood, N., et al. (2008) Potential effects of introducing
behind the counter drugs. RAND Working Paper.
[8] Sood, N., Sun, E., et al. (2012). Behind-the-counter stat-
ins: A silver bullet for reducing costs and increasing ac-
cess? Health Services Research, 47, 174-187.
http://dx.doi.org/10.1111/j.1475-6773.2011.01315.x
[9] Gurwitz, J.H., McLaughlin, T.J., et al. (1995). The effect
of an Rx-to-OTC switch on medication prescribing pat-
terns and utilization of physician services: The case of

C. St omberg et al. / Health 5 (2013) 1667-1680 1679
vaginal antifungal products. Health Services Re sea rch, 30,
672-685.
[10] Sullivan, P.W., Nair, K.V., et al. (2005) The effect of the
Rx-to-OTC switch of loratadine and changes in prescrip-
tion drug benefits on utilization and cost of therapy. Ame-
rican Journal of Managed Care, 11, 374-382.
[11] Furler, M.D., Rolnick, M.S., et al. (2002) Cost impact of
switching histamine(2)-receptor antagonists to nonprescrip-
tion status. The Annals of Pharmacotherapy, 36, 1135-
1141. http://dx.doi.org/10.1345/aph.1A231
[12] Filion, K., et al. (2007) The impact of over-the-counter
simvastatin on the number of statin prescriptions in the
United Kingdom: A view from the general practice re-
search database. Pharmacology and Drug Safety, 16, 1-4.
http://dx.doi.org/10.1002/pds.1314
[13] Walker, R. and Hinchliffe, A. (2010) Prescribing and sale
of ophthalmic chloramphenicol following reclassification
to over-the-counter availability. International Journal of
Pharmacy Practice, 18, 269-274.
http://dx.doi.org/10.1111/ijpp.18.05.00046.x
[14] Sullivan, P.W., Follin, S.L., et al. (2003) Transitioning the
second-generation antihistamines to over-the-counter
status: A cost-effectiveness analysis. Medical Care, 41,
1382-1395.
http://dx.doi.org/10.1097/01.MLR.0000100584.18276.C4
[15] Stead, L.F., et al. (2008) Nicotine replacement therapy for
smoking cessation. Cochrane Database of Systematic Re-
views, 1, Article ID: CD000146.
http://dx.doi.org/10.1002/14651858.CD000146.pub3
[16] Raymond, E.G., Trussell, J., et al. (2007). Population ef-
fect of increased access to emergency contraceptive pills:
A systematic review. Obstetrics & Gynecology, 109, 181-
188.
http://dx.doi.org/10.1097/01.AOG.0000250904.06923.4a
[17] Polis, C.B., Schaffer, K., et al. (2007). Advance provision
of emergency contraception for pregnancy prevention: A
meta-analysis. Obstetrics & Gynecology, 110, 1379-1388.
http://dx.doi.org/10.1097/01.AOG.0000295603.84568.f6
[18] Lakdawalla, D., et al. (2006) Intellectual property and
marketing. NBER Working Paper.
[19] Brass, E.P. (2004) Consumer behavior in the setting of
over-the-counter statin availability: Lessons from the
consumer use study of OTC Mevacor. American Journal
of Cardiology, 94, 22F-29F.
http://dx.doi.org/10.1016/j.amjcard.2004.07.051
[20] Melin, J.M., Struble, W.E., et al. (2004) A consumer use
study of over-the-counter lovastatin (CUSTOM). Ameri-
can Journal of Cardiology, 94, 1243-1248.
http://dx.doi.org/10.1016/j.amjcard.2004.08.007
[21] Brass, E.P., et al. (2008) Can consumers self-select for
appropriate use of an over-the-counter statin? The self
evaluation of Lovastatin to enhance cholesterol treatment
study. American Journal of Cardiology, 101, 1448-1455.
http://dx.doi.org/10.1016/j.amjcard.2008.01.020
[22] Kline and Company (2010) Rx-to-OTC Forecasts USA
2010. Kline & Company, Inc., Report no. #Y406C.
http://www.klinegroup.com/reports/brochures/y406c/broc
hure.pdf
[23] Brass, E.P., et al. (2006) Potential impact on cardiovas-
cular public health of over-the-counter statin availability.
American Journal of Cardiology, 97, 851-856.
http://dx.doi.org/10.1016/j.amjcard.2005.10.022
[24] Straka, R., et al. (2007) Economic impacts attributable to
the early clinical benefit of atorvastatin therapy—A US
managed care perspective. Current Medical Research and
Opinion, 23, 1517-1529.
http://dx.doi.org/10.1185/030079907X199808
[25] Cardozo, L., et al. (2010) The cost-effectiveness of soli-
fenacin vs fesoterodine, oxybutynin immediate-release,
propiverine, tolterodine, extended-release and tolterodine
immediate-release in the treatment of patients with over-
active bladder in the UK National Health Service. British
Journal of Urology International, 106, 506-514.
http://dx.doi.org/10.1111/j.1464-410X.2009.09160.x
[26] Clemens, J. (2011) Factors associated with work produc-
tivity impairment in subjects with overactive bladder.
Presentation, AMCP 23rd Annual Meeting, Minneapolis,
27-29 April 2011.
[27] US Census Bureau (2010) Income, poverty, and health
insurance coverage in the United States: 2009. Bureau of
Labor Statistics, Labor Force Participation Rate Series.
[28] Lipton, R., et al. (2007) Migraine prevalence, disease
burden, and the need for preventative therapy. Neurology,
68, 343-350.
http://dx.doi.org/10.1212/01.wnl.0000252808.97649.21
[29] Hu, X., et al. (1999) Burden of migraine in the United
States—Disability and economic costs. Archives of Inter-
nal M edicine, 159, 813-818.
http://dx.doi.org/10.1001/archinte.159.8.813
[30] Lipton, R., et al. (2002) Migraine in the United States:
Epidemiology and patterns of access to care. Neurology,
58, 885-894. http://dx.doi.org/10.1212/WNL.58.6.885
[31] Cambpell, N., et al. (2012) Internet-ordered viagra (sil-
denafil citrate) is rarely genuine. Presentation, American
Urological Association 2012 Annual Meeting, Atlanta,
19-23 May 2012.
[32] Jackson, G., et al. (2010) Counterfeit phosphodisterase
type 5 inhibitors pose significant safety risks. Interna-
tional Journal of Clinical Practice, 64, 497-504.
http://dx.doi.org/10.1111/j.1742-1241.2009.02328.x
[33] Smith, K. and Roberts, M. (2000) The Cost-Effectiveness
of Sildenafil. The Annals of Internal Medicine, 132,
933-937.
http://dx.doi.org/10.7326/0003-4819-132-12-200006200-
00002
[34] Shaw, M.J., Fendrick, A.M., et al. (2001) Self-reported
effectiveness and physician consultation rate in users of
over-the-counter histamine-2 receptor antagonists. The
American Journal of Gastroenterologyl, 96, 673-676.
http://dx.doi.org/10.1111/j.1572-0241.2001.03602.x

C. St omberg et al. / Health 5 (2013) 1667-1680
1680
APPENDIX: MATHEMATICAL
TREATMENT
The interrupted time series approach used here is mo-
tivated by the idea that utilization pre/post OTC switch
can be compared after properly conditioning on other
potentially predictive variables. In particular, we are in-
terested in identifying a break in either the utilization of
molecule i in class j at time t, qijt, or its class,
j
tiijt
coincident with OTC switch. Suppose that
utilization of the molecule is governed by the following
process:
Q q

ijtijt ijt
qfX
where Xijt is a vector of pre-determined variables that are
potentially predictive of utilization. By pre-determined
we mean that the variables in question are known at time
t, and are not affected by current utilization. These could
include, for example, time trends, seasonal effects, life-
cycle indicators, and even past observations of utilization.
The function

·
f
is possibly nonlinear, and ijt  is a
random disturbance with mean zero assumed to be un-
correlated with Xijt. Suppose, in addition, that utilization
in presence of an OTC product is governed by the fol-
lowing alternative process:

.
O
ijtijt ijt
qg uX
where ijt is another random disturbance that is uncor-
related with ijt . Our approach to identifying the break
associated with an OTC switch therefore amounts to
estimating the expected difference in utilization under
the two alternative regimes given the information we
have in Xijt:
u


.
O
ijt ijtijtijt ijt
ijt ijt
EdEq q
Eg f








XX
XX
Since OTC switches occur at a point in time, the
comparison of


·
f
and

·
g
is counterfactual in that
one observes


·
f
before the switch, and


·
g
after-
ward, but never both at the same time. The econometric
question therefore boils down to a test of whether, given
the observed conditioning information Xijt, there is
discernible difference between

·
f
as estimated in the
pre-OTC period, and


·
g
as estimated in the post-OTC
period.
The difference Eijt d



 can be estimated directly by
comparison of two models. This can be achieved, for
example, by use of dummy variables that allow for direct
estimation of changes in slope and intercept coefficients
within a linear model. But, it can also be achieved simply
by comparing forecasts of utilization from an estimate of


·
f
based on data prior to switch to actual post-switch
data. In other words, we can compare to
O
ijt
q


·
f
since


O
ijtijt ijtijtijt
Eq fEgf






XXX X

It is important to recognize that the interrupted time
series approach employed here can be sensitive to the
quality of the conditioning information in Xijt. If this in-
formation set does not properly account for important
factors that affect utilization, then the comparison be-
tween


·
f
and


·
g
may be confounded by omitted
variables, or lead to omitted variables bias. Similarly,
functional form misspecification of our estimates of


·
f
and


·
g
could also lead to improper inference.