Mary Jo V. Pugh, PhD
Dale Hesdorffer, PhD
Chen-Pin Wang, PhD
Megan E. Amuan, MPH
Jeffrey V. Tabares, MS
Erin P. Finley, PhD
Joyce A. Cramer, BS
Andres M. Kanner, MD
Craig J. Bryan, PsyD
Editorial, page 1889
Temporal trends in new exposure to
antiepileptic drug monotherapy and
Objective: Because some recent studies suggest increased risk for suicide-related behavior (SRB;
ideation, attempts) among those receiving antiepileptic drugs (AEDs), we examined the temporal
relationship between new AED exposure and SRB in a cohort of older veterans.
Methods: We used national Veterans Health Administration databases to identify veterans aged
$65 years who received a new AED prescription in 2004–2006. All instances of SRB were
identified using ICD-9-CM codes 1 year before and after the AED exposure (index) date. We also
identified comorbid conditions and medication associated with SRB in prior research. We used
generalized estimating equations with a logit link to examine the association between new AED
exposure and SRB during 30-day intervals during the year before and after the index date, con-
trolling for potential confounders.
Results: In this cohort of 90,263 older veterans, the likelihood of SRB the month prior to AED
exposure was significantly higher than in other time periods even after adjusting for potential con-
founders. Although there were 87 SRB events (74 individuals) the year before and 106 SRB
events (92 individuals) after, approximately 22% (n 5 16) of those also had SRB before the index
date. Moreover, the rate of SRB after AED start was gradually reduced over time.
Conclusions: The temporal pattern of AED exposure and SRB suggests that, in clinical practice,
the peak in SRB is prior to exposure. While speculative, the rate of gradual reduction in SRB there-
after suggests that symptoms may prompt AED prescription. Neurology®2013;81:1900–1906
AED 5 antiepileptic drug; FDA 5 US Food and Drug Administration; FY 5 fiscal year; GEE 5 general estimating equations;
ICD-9-CM 5 International Classification of Diseases, 9th revision, Clinical Modification; OR 5 odds ratio; PTSD 5 posttraumatic
stress disorder; SRB 5 suicide-related behavior; VA 5 Veterans Health Administration.
suicide-related behavior (SRB) in 2008,1study findings examining SRB among AED users have
been inconsistent. Some found increased risk for different individual AEDs,2,3while others found
that increased risk associated with AED exposure was primarily associated with depression,4was
not significant, or was attenuated after controlling for psychiatric comorbidity.3,5–7These findings
depression,chronic pain) are prescribed AEDs to address symptoms ofthese conditions.8–11Simon
et al.12conducted a temporal analysis examining patterns of SRB before and after initiation of 3
types of therapy—antidepressant therapy in primary care, antidepressant therapy in psychiatric
specialty care, or psychotherapy without antidepressant—and found that SRB peaked just before
the initiation of therapy under all 3 conditions, supporting the possibility of confounding by
indication in studies linking antidepressant prescriptions to SRB.12
From the South Texas Veterans Health Care System (VERDICT) (M.J.V.P., C.-P.W., J.V.T., E.P.F.), San Antonio; Department of Epidemiology
& Biostatistics (M.J.V.P., C.-P.W.), University of Texas Health Science Center at San Antonio; Department of Medicine (M.J.V.P.), Texas A & M
University, College Station; Mailman School of Public Health (D.H.), Sergievsky Center, The Gertrude H. Sergievsky Center, College of Physicians
and Surgeons, Columbia University, New York, NY; Edith Nourse Rogers Memorial Hospital (The Center for Health Quality, Outcomes and
Economic Research [CHQOER]) (M.E.A.), Bedford, MA; Yale University School of Medicine (J.A.C.), New Haven, CT; Epilepsy Therapy Project
(J.A.C.), Houston, TX; Rush University Medical Center (A.M.K.), Chicago, IL; and National Center for Veterans Studies (C.J.B.), University of
Utah, Salt Lake City.
Disclaimer: The content of this article is solely the responsibility of the authors and does not necessarily reflect the official views of the Veterans
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
1900 © 2013 American Academy of Neurology
In a recent study, we examined the associa-
tion of new AED exposure and SRB in an old-
er population since the FDA meta-analysis was
unable to adequately address differences in
SRB in the geriatric population.1We found
a significant association between AED expo-
sure and SRB even after controlling for mood
disorders and prior SRB.8However, we did
not systematically examine the temporal rela-
tionship between SRB and AED exposure.
This study used a technique similar to the
approach of Simon et al. to examine the tem-
poral relationship between SRB and AED
exposure in that cohort of older veterans.12
METHODS We conducted a retrospective cohort study to
examine the temporal relationship between new AED monother-
apy exposure and SRB in older veterans.13Given the limitations
inherent in observational data analysis, we selected a new user
monotherapy design, assessing the SRB 1 year before and 1 year
after the AED exposure date among new AED users. This design
can reduce the potential “survivor” bias that occurs when patients
with chronic use are more likely to better tolerate a drug, and
remain in the cohort.13Moreover, new exposure designs allow the
study to assign an index date to those with exposure, thus avoid-
ing “chronology” bias similar to development of an inception
cohort in disease epidemiology.13While the characteristics of
the new user design are a strength for these reasons, it does not
really allow assessment of polytherapy combinations unless that
combination is prescribed on the index treatment day, which
occurred in fewer than 1% of new users in this cohort.
Standard protocol approvals, registrations, and patient
consents. Institutional review board approval was obtained from
the University of Texas Health Science Center San Antonio.
Data source. Data included national Veterans Health Administra-
tion (VA) pharmacy and diagnostic data for individuals aged 65 years
or older receiving care between fiscal year (FY) 2004 (October 1,
2003–September 30, 2006) and FY2006. To ensure accuracy in
identifying comorbid conditions and medication use, we required
that participants have at least 1 year of VA care prior to the index
date (first AED initiation during the study period). In order to allow
1-year follow-up after all exposures, our data also included FY2007.
Thus, data included in this study were from FY2003 to FY2007.
Exposure to seizure medications. We used the VA product
name to identify prescription of any of the following AEDs included
and 2006: phenobarbital, phenytoin, carbamazepine, valproate, gaba-
pentin, lamotrigine, levetiracetam, oxcarbazepine, tiagabine, topira-
mate, and pregabalin. Individuals receiving a first (monotherapy)
prescription for any of these AEDs in the VA pharmacy data between
antee that no AED prescriptions occurred ever prior to our study
period, given the 1-year clean period, this index date is hereafter
termed first AED exposure. Individuals with prior use of AEDs in
the year before the study period and those prescribed multiple AEDs
during the study period were excluded from the cohort.
Suicide-related behavior. SRB was defined using ICD-9-CM
codes (V62.84: suicidal ideation, E950–E958: attempt, com-
pleted suicide, and self-inflicted injury) in VA inpatient and
outpatient data files 1 year prior to and 1 year after the index
date. We identified the date of all SRB episodes and identified
SRB for participants as a dichotomous variable during 24 periods
(1 for each 30-day period before and after the index date). Con-
sistent with the procedure used by Simon et al.12and others,3the
index date was included in the period 30 days prior to the index
date. However, we also conducted analyses (not reported here)
including the index day in the period 30 days after the index date
to determine whether results were consistent.
Demographic covariates. We used VA inpatient and outpa-
tient data to identify demographic characteristics associated with
AED exposure, including age (65–74 years, 75–84 years, 85 years
and older), sex, race/ethnicity (non-Hispanic white, black, His-
panic, other), and an indicator of poverty where those with very
low income do not have copayment (VA means test).14
Clinical covariates. Baseline clinical characteristics were identi-
fied during the year prior to AED exposure. We included indica-
tors of disease states or conditions that have previously been
associated with SRB. These included the following psychiatric
diagnoses: depression, anxiety, bipolar disorder, posttraumatic
stress disorder (PTSD), substance abuse or dependence, and
schizophrenia using previously validated ICD-9-CM codes.14–16
Finally, we evaluated other conditions that have been previously
associated with AED use or SRB, including epilepsy, chronic pain
conditions (migraine, back pain, neuropathic pain), and demen-
tia.17–20Additional indicators of psychiatric comorbidity included
in previous analyses (prior prescription of any antidepressant or
antipsychotic medication and prior psychiatric hospitalization)6
were not included in this analysis due to collinearity with the
temporal assessment of SRB and mental health diagnosis.
Analysis. We first described characteristics of individuals with
new AED exposure including the proportion of individuals
who had SRB before and after the index date using frequencies
and percentages for categorical variables or means and SDs for
continuous variables. General estimating equations (GEE) analy-
ses21compared the likelihood of SRB during each 30-day period,
using the period 30 days after the index date as the reference
period. GEE was used to account for the correlation among the
repeated measures of SRB within each individual. We modeled
the log-oddsof SRB occurrence as a linear functionof timeperiod
and covariates described above and report the adjusted odds ratios
(ORs) and 95% confidence intervals. Analyses were conducted
with Proc Genmod using SAS (SAS 9.2 Ts Level 2M3, 2002–
2003, SAS Institute, Cary, NC).
RESULTS From the overall population aged 65 years
and older receiving VA care FY2004–2006 (n 5
2,430,186), 283,012 were excluded due to chronic/
multiple AED use or inadequate data (e.g., no VA care
each year of the study period) prior to meeting age
or AED exposure criteria. Of those remaining,
90,230 individuals (4.2% of the overall population)
received a first AED monotherapy prescription
between FY2004 and FY2006 without a previous
AED prescription in FY2003. Table 1 shows descrip-
tive statistics overall for those with a first AED expo-
sure. Participants were primarily male (97.3%) with
a mean age of 75.1 years (SD 6.1). The majority
(76.2%) of those receiving a first AED monotherapy
received gabapentin (n 5 68,725). Valproate (n 5
5,833) was second most common, followed by
Neurology 81November 26, 2013 1901
phenobarbital/primidone (n 5 5,289), phenytoin
(n 5 4,136), carbamazepine (n 5 2,976), and top-
iramate (n 5 1,516). Fewer than 0.1% of the cohort
received each of the following AEDs: lamotrigine
(n 5 815), levetiracetam (n 5 636), oxcarbazepine
(n 5 169), pregabalin (n 5 83), tiagabine (n 5 53),
and zonisamide (n 5 32).
Individuals with a first AED monotherapy com-
iety, bipolar disorder, PTSD, schizophrenia, substance
abuse/dependence, conditions associated with chronic
pain, and dementia. Psychiatric hospitalization and
prescriptions for antidepressant or antipsychotic medi-
cations in the previous year were also common. These
rates were significantly higher than found in similar
veterans without AED exposure (data not shown).22
Table 1 shows bivariate statistics comparing char-
acteristics of individuals with and without SRB after
first AED prescription. Those with subsequent SRB
were more likely to be younger, white, unmarried,
and with income below the VA poverty indicator.
These individuals also were more likely to have diag-
noses of depression, anxiety, bipolar disorder, PTSD,
schizophrenia, substance abuse, and dementia. They
were also more likely to have prior SRB during the
year prior to AED prescription.
Figure 1 shows the temporal patterns of SRB
among those with a first AED exposure. The clear
peaks of SRB prior to AED exposure suggest that
the SRB was most common prior to the AED pre-
scription. However, SRB continued to occur after
AED exposure. Overall, there were more SRB at-
tempts after the index date (n 5 106 episodes in 92
individuals) than before the index date (n 5 84 epi-
sodes in 74 individuals). Approximately 22% of those
who had SRB prior to the index date also had SRB
after the index date (16 of 74; OR 5 327 compared
to those without prior SRB).
Table 1 Characteristics of those who received a new antiepileptic drug monotherapy between FY2004 and
No SRB after AED,
n 5 90,171, % (n)
SRB after AED,
n 5 92, % (n)p Value
Age, y, mean (SD)
75.1 (6.1) 73.3 (6.3)
97.8 (88,195) 96.7 (89) 0.48
70.2 (63,332)84.8 (78)
7.5 (6,764)6.5 (6)
4.5 (4,062) 4.4 (4)
17.8 (16,013)4.3 (4)
67.4 (60,748)42.4 (39)
71.3 (64,295)83.7 (77)
Clinical characteristics defined prior to
prescription of seizure medication
0.1 (67) 17.4 (16)
15.8 (14,230) 66.3 (61)
8.0 (7,205)27.2 (25)
2.0 (1,792) 25.0 (23)
Posttraumatic stress disorder
4.4 (4,004) 14.1 (13)
1.3 (1,128)9.8 (9)
3.3 (2,953) 23.9 (22)
7.4 (6,626)16.3 (15)
Prior psychiatric inpatient care
0.8 (733)31.5 (29)
62.2 (56,064)64.1 (59)0.70
3.6 (3,225)6.5 (6)0.13
Abbreviations: AED 5 antiepileptic drug; FY 5 fiscal year; SRB 5 suicide-related behavior.
Age analysis 2-tailed t test; all others x2statistic.
aDenotes p , 0.01.
1902Neurology 81 November 26, 2013
GEE analyses compared the likelihood of SRB dur-
ing the 24 time periods controlling for demographic
and clinical characteristics, using the period 30 days
after the index date as the reference time period.
Among those with a first AED exposure, SRB was sig-
nificantly less likely in every time period compared to
the index period (figure2). Findingswere similarwhen
the index date was included in the 30 days after
Table 2 shows the ORs for the demographic and
clinical characteristics included in the GEE analysis of
SRB over time. These findings suggest that depression
Figure 1 Temporal relationship of seizure medication index date and suicide-related behavior
Suicide-related behavior (SRB) before and after receipt of antiepileptic drugs (AEDs) among older veterans. The index date
(hashed line) indicates the patient’s receipt of AEDs. Values to the left of the index date show the incidence of SRB up to 12
months before receipt of AEDs. Values to the right of the index date show incidence of SRB up to 12 months after receipt of
Figure 2 Suicide-related behavior in relation to the month before seizure medication prescription
Graphedadjustedoddsratios showthelikelihoodofsuicide-relatedbehaviorbeforeand aftertheindexperiod(30dayspriorto
and including the date of first antiepileptic drug prescription); odds ratios adjusted for variables described in table 1.
Neurology 81November 26, 2013 1903
and bipolar disorder remained significant predictors of
SRB in the multivariable temporal model.
DISCUSSION We found that individuals receiving a
first AED during our study period were more likely to
have SRB during the 30 days prior to AED exposure
than any othertime period in theyear before and after
exposure. These findings are similar tothose ofSimon
et al.12in a sample of patients with depression. That
study found that, regardless of the type of treatment
(antidepressant or psychotherapy) or type of provider
(primary care, psychiatrist, psychologist), SRB was
most common prior to the onset of treatment with
a gradual reduction in SRB after the effects of the
treatment were realized.
In a prior analysis, we found that individuals with
a first AED exposure had increased risk of SRB after
exposure22even when controlling for prior SRB. This
is consistent with our current findings, in which we
found that there were more episodes of SRB after
exposure than before exposure. However, in our cur-
rent analysis, we found that SRB peaked prior to
AED prescription and that rates of SRB were signif-
icantly lower in all periods before and after the index
date, which included the 30 days before initiating
treatment as well as the first day of treatment. More-
over, the only statistically significant clinical condi-
tions in the multivariate model were depression and
bipolar disorder. Rather than highlighting the associ-
ation between SRB and AED exposure, these data
suggest that individuals who received the AED were
at greater risk of SRB both before and after initiation
of AED, even after controlling for psychiatric
This is also consistent with the findings of Gib-
bons and colleagues,7who found that the risk of
SRB was not increased for nonpsychiatric populations
(e.g., those with epilepsy or pain indications). More-
over, they found that SRB decreased after treatment
with gabapentin. Additionally, our findings account
for methodologic considerations not included in
Gibbons et al.7that limited their work23: standardized
assessment of suicidality (i.e., ICD-9-CM codes were
documented in medical records only if the patient
sought care for the specific condition) and full defi-
nition of suicidiality (i.e., our definition of SRB
included ideation, attempts, completed suicides, and
These findings for epilepsy are, however, contrary
to the FDA analysis and other studies where epilepsy
was a risk factor for SRB, though methodologic dif-
ferences may help explain this finding. The outcome
for this study is SRB throughout a 24-month study
period—12 months before AED prescription and
12 months after AED prescription. Epilepsy patients
included in the clinical trials of AEDs were individu-
als with chronic, uncontrolled epilepsy, and the AED
was tested as add-on therapy. These data suggest that
new-onset epilepsy may incur less SRB risk than
Finally, while a number of other demographic
characteristics were significant in bivariate analyses,
only marital status (a proxy measure of psychosocial
status) was statistically significant in the multivariable
model, which controlled for comorbidity previously
associated with SRB.6
Our findings must be interpreted in light of several
limitations. First, we were not able to identify the spe-
cialty of the AED prescriber. Second, our approach
combined ICD-9-CM codes for suicidal ideation and
attempts. We did not have other data that may have
stantially rarer than suicidal ideation or attempt,24so
our analysis may be less informative when trying to
understand whether AEDs are associated with suicide
completion. Moreover, the administrative data may
have limited our ability to identify all cases of SRB
where chart review, death index review, or interview
methods might have revealed more patients with this
outcome. Kim and colleagues25found that the speci-
ficity of these codes was high (.90%) but sensitivity
was poor. Additionally, administrative datasets pro-
vided limited insight about psychosocial patient fac-
tors. In this case, we found that those who were
married were significantly less likely to have SRB,
but those under the poverty limit were not at increased
risk compared to those above the poverty limit. Our
data could not account for other psychosocial factors
(e.g., living conditions/psychosocial stressors, or
changes in such factors) around SRB.
Our new user design restricted the number of indi-
viduals included, thus limiting our ability to examine
Table 2 Odds ratios predicting suicide-related behavior after antiepileptic drug
prescription: Covariates in temporal analysis
Variable Odds ratio
Men (vs women)
Indicator of poverty
Epilepsy (vs no epilepsy)
Neuropathic pain diagnosis
Posttraumatic stress disorder
Substance use disorder
1904Neurology 81 November 26, 2013
difference by specific AED. It also excluded those who
received multiple AEDs. As described earlier, a chronic
use cohort introduces several types of bias that limit the
ability to evaluate the association of a specific drug
exposure on outcomes.13Our use of a 1-year clean
period for AEDs for study inclusion likely resulted in
the inclusion of individuals who had been previously
exposed to AEDs. Though the exact proportion is
unknown, our prior results are similarevenafter adjust-
ing for prior use of AEDs.
Finally, the study population consisted of older
VA patients who were primarily men. The geriatric
population is a strength since the FDA meta-analysis
was limited by inclusion of few older patients in clin-
ical trials. However, prior research of antidepressants
and AEDs have found reduced risk of SRB in the
elderly.26Thus, findings may not generalize to youn-
ger patients or women. The racial composition of the
sample was similar to the racial composition of older
Americans more broadly.27
Although prior studies have raised concerns regard-
ing increased risk for SRB among those receiving
AEDs, our findings, and those of other studies that
have found recurrence of SRB ranging from 20% to
50% overall and 11% to 14% for those over age 50,
it is appropriate to be cautious in interpreting studies
that do not examine and address SRB occurring prior
to AEDs.28–30As the risk for recurrent SRB was 22%
in individuals with SRB prior to AEDs, these patients
should be followed closely to prevent recurrent SRB.
Mary Jo V. Pugh: designed the study, obtained funding, interpretation of
analysis, and drafted manuscript. Dale Hesdorffer: interpretation of data
and revised manuscript. Chen-Pin Wang: study design, data analysis, crit-
ical review of manuscript. Megan E. Amuan: data analysis, data acquisi-
tion, interpretation of analysis. Jeffrey Tabares: interpretation of data and
revised manuscript. Erin P. Finley: interpretation of data and revised
manuscript. Joyce A.: interpretation of data and revised manuscript.
Andres M. Kanner: interpretation of data and revised manuscript. Craig
J. Bryan: conceptualization of study, study design, interpretation of anal-
ysis, and critical review of manuscript.
The authors thank Kathleen Franklin for manuscript preparation. The
views expressed in this article are those of the authors and do not necessar-
ily represent the views of the Department of Veterans Affairs.
Supported by the South Texas Veterans Healthcare System/Audie L.
Murphy Division and the VERDICT research program; and the VA
Health Services Research and Development Service IIR 06-062. The
funding organizations had no role in the design and conduct of the study;
the collection, management, analysis, and interpretation of the data; or
the preparation, review, or approval of the manuscript.
M. Pugh reports no disclosures. D. Hesdorffer serves on the editorial
board of Epilepsy and Behavior and Epilepsy Research and as contributing
editor to Epilepsy Currents. She co-chairs the Commission on Epidemi-
ology for the International League Against Epilepsy. She is a consultant
for the Mount Sinai Medical Center, Injury Prevention Center.
Dr. Hesdorffer received a travel award from GlaxoSmithKline in 2010.
In 2012, she participated in advisory boards for UCB, UpsherSmith, and
Esai. She is funded by grants from CDC, DP002209, PI, 2009–2014;
AUCD, RT01, Co-I (PI of Columbia subcontract), 2008–2012;
National Institute of Neurological Disorders and Stroke, NS31146,
Co-I (PI of Columbia subcontract), 2007–2014; National Institute of
Neurological Disorders and Stroke, NS043209, Co-I (PI of Columbia
subcontract), 2003–2013; CDC, MM1002, Co-I, 2006–2010; National
Institute of Neurological Disorders and Stroke, 5U01NS04911, Co-I (PI
of Columbia subcontract), 2011–2012; National Institute of Neurolog-
ical Disorders and Stroke, NS078419, Co-I, 2012–2015; and the Epi-
lepsy Foundation of America 2010–2012. C. Wang, M. Amuan,
J. Tabares, E. Finley, and J. Cramer report no disclosures. A. Kanner
reports the following disclosures: Consultant honorarium from Neuro-
pace (2013). Member of Data safety Board: Vertex Laboratories (2012,
2013). Member Editorial Boards: Epilepsy Currents, Epilepsy & Behavior,
CNS Spectrum, and Epileptology. Research grants from GlaxoSmithKline
(last one in 2010), Novartis (2010), and Pfizer (2011). Royalties for
Psychiatric Issues in Epilepsy, Second Edition: A Practical Guide to Diagnosis
and Treatment (Lippincott Williams & Wilkins, 2006); Controversial
Issues in Psychiatric Aspects of Epilepsy (Elsevier, 2008); and Depression
in Neurologic Disorder (Wylie & Blackwell 2013). C. Bryan reports grant
funding from the Department of Defense and the Department of the Air
Force; consulting fees from Kognito Interactive; consulting fees from
Intelligent Automation, Inc.; honoraria from CMI Education; and roy-
alties from Springer Publishing. Go to Neurology.org for full disclosures.
Received March 12, 2013. Accepted in final form August 16, 2013.
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This Week’s Neurology®Podcast
Temporal trends in new exposure to antiepileptic drug mono-
therapy and suicide-related behavior (See p. 1900)
This podcast begins and closes with Dr. Robert Gross, Editor-in-
Chief, briefly discussing highlighted articles from the November
talks with Dr. Mary Jo Pugh about her paper on temporal trends in
new exposure to antiepileptic drug monotherapy and suicide-related
behavior.Dr.RoyStrowd readsoure-Pearlofthe weekaboutGarcin
syndrome. In the next part of the podcast, Dr. Shanna Patterson
focuses her interview with Dr. Jon Stone on treatment of functional
neurologic symptoms. Disclosures can be found at www.neurology.org.
At www.neurology.org, click on “RSS” in the Neurology Podcast box to listen to the most recent
podcast and subscribe to the RSS feed.
CME Opportunity: Listen to this week’s Neurology Podcast and earn 0.5 AMA PRA Category 1
CME Credits™ by answering the multiple-choice questions in the online Podcast quiz.
1906 Neurology 81 November 26, 2013