Standards for epidemiologic studies and surveillance of epilepsy

CDC National Center for Chronic Disease Prevention and Health Promotion, Atlanta, Georgia, USA.
Epilepsia (Impact Factor: 4.57). 09/2011; 52 Suppl 7(s7):2-26. DOI: 10.1111/j.1528-1167.2011.03121.x
Source: PubMed


Worldwide, about 65 million people are estimated to have epilepsy. Epidemiologic studies are necessary to define the full public health burden of epilepsy; to set public health and health care priorities; to provide information needed for prevention, early detection, and treatment; to identify education and service needs; and to promote effective health care and support programs for people with epilepsy. However, different definitions and epidemiologic methods complicate the tasks of these studies and their interpretations and comparisons. The purpose of this document is to promote consistency in definitions and methods in an effort to enhance future population-based epidemiologic studies, facilitate comparison between populations, and encourage the collection of data useful for the promotion of public health. We discuss: (1) conceptual and operational definitions of epilepsy, (2) data resources and recommended data elements, and (3) methods and analyses appropriate for epidemiologic studies or the surveillance of epilepsy. Variations in these are considered, taking into account differing resource availability and needs among countries and differing purposes among studies.


Available from: Charles Newton, Sep 14, 2014
Standards for epidemiologic studies and
surveillance of epilepsy
David J. Thurman,
Ettore Beghi,
Charles E. Begley,
Anne T. Berg,
Jeffrey R. Buchhalter,
Ding Ding,
Dale C. Hesdorffer,
W. Allen Hauser,
Lewis Kazis,
Rosemarie Kobau,
Barbara Kroner,
David Labiner,
Kore Liow,
Giancarlo Logroscino,
Marco T. Medina,
Charles R. Newton,
Karen Parko,
Angelia Paschal,
Pierre-Marie Preux,
Josemir W. Sander,
Anbesaw Selassie,
William Theodore,
rn Tomson, and
Samuel Wiebe for the ILAE
Commission on Epidemiology
CDC National Center for Chronic Disease Prevention and Health Promotion, Atlanta, Georgia, U.S.A.;
Laboratory of Neurological Disorders, Institute for Pharmacological Research ‘‘Mario Negri,’’ Milan, Italy;
of Health Services Research and Department of Management and Policy Sciences, School of Public Health,
University of Texas Health Sciences Center, Houston, Texas, U.S.A.;
Department of Biology, Northern Illinois
University, DeKalb, Illinois, U.S.A.;
Chicago-Northwestern Children’s Memorial Hospital, Epilepsy Center,
Chicago, Illinois, U.S.A.;
Department of Child and Adolescent Neurology, Mayo Clinic, Scottsdale, Arizona,
Department of Biostatistics and Epidemiology, Institute of Neurology, Fudan University, Shanghai, China;
Gertrude H. Sergievsky Center and Department of Epidemiology, Columbia University, New York, New York,
Department of Neurology, Columbia University, New York, New York, U.S.A.;
Department of Health
Policy & Management, Boston University School of Public Health, Boston, Massachusetts, U.S.A.;
Center for Chronic Disease Prevention and Health Promotion, Atlanta, Georgia, U.S.A.;
RTI International,
Rockville, Maryland, U.S.A.;
Department of Neurology, The University of Arizona, Tucson, Arizona, U.S.A.;
Department of Internal Medicine (Neurology), University of Hawaii John Burns School of Medicine, Honolulu,
Hawaii, U.S.A.;
Department of Neurology, University of Bari, Bari, Italy;
Faculty of Medical Sciences, National
Autonomous University of Honduras, Tegucigalpa, Honduras;
Kenya Medical Research Institute/Wellcome Trust
Collaborative Programme, Kilifi, Kenya;
Institute of Child Health, University College of London, London, United
Department of Neurology, UCSF School of Medicine, San Francisco, California, U.S.A.;
of Health & Kinesiology, Mississippi University for Women, Columbus, Mississippi, U.S.A.;
University of Limoges,
Institute of Neuroepidemiology and Tropical Neurology and Tropical and Comparative Neuroepidemiology,
Limoges, France;
UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom;
Epilepsy Institutes of the Netherlands Foundation, Heemstede, The Netherlands;
Department of Medicine,
Medical University of South Carolina, Charleston, South Carolina, U.S.A.;
Clinical Epilepsy Section, National
Institutes of Health, Bethesda, Maryland, U.S.A.;
Department of Neurology, Karolinska University Hospital,
Stockholm, Sweden; and
Department of Clinical Neurological Sciences & Hotchkiss Brain Institute,
University of Calgary, Calgary, Alberta, Canada
Worldwide, about 65 million people are esti-
mated to have epilepsy. Epidemiologic studies
are necessary to define the full public health bur-
den of epilepsy; to set public health and health
care priorities; to provide information needed
for prevention, early detection, and treatment;
to identify education and service needs; and to
promote effective health care and support pro-
grams for people with epilepsy. However, differ-
ent definitions and epidemiologic methods
complicate the tasks of these studies and their
interpretations and comparisons. The purpose of
this document is to promote consistency in defi-
nitions and methods in an effort to enhance
future population-based epidemiologic studies,
facilitate comparison between populations, and
encourage the collection of data useful for the
promotion of public health. We discuss: (1) con-
ceptual and operational definitions of epilepsy,
(2) data resources and recommended data
Address correspondence to David J. Thurman, MD, MPH, CDC
National Center for Chronic Disease Prevention and Health Promotion,
4770 Buford Highway, NE, Mailstop K51, Atlanta, GA, U.S.A. E-mail:
Wiley Periodicals, Inc.
ª 2011 International League Against Epilepsy
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
Page 1
elements, and (3) methods and analyses appro-
priate for epidemiologic studies or the surveil-
lance of epilepsy. Variations in these are
considered, taking into account differing
resource availability and needs among countries
and differing purposes among studies.
KEY WORDS: Epidemiology, Surveillance, Epi-
Worldwide, there are an estimated at least 65 million
people living with epilepsy (Ngugi et al., 2010). Reported
estimates of epilepsy occurrence vary substantially among
populations studied, but, in sum, indicate that in devel-
oped countries, the annual incidence of epilepsy is nearly
50 per 100,000 population, whereas the prevalence
approximates 700 per 100,000 (Hirtz et al., 2007). In low
and middle income countries, estimates of the correspond-
ing rates are generally higher (Hauser, 1995; Kotsopoulos
et al., 2002; Sander, 2003; Burneo et al., 2005b; Preux &
Druet-Cabanac, 2005; Ngugi et al., 2010). Throughout the
world, therefore, epilepsy imposes a substantial public
health burden.
The term ‘‘epileps y ’’ encompasses many specific
conditions in which unprovoked seizures occur that
may have varying etiology, risk factors, and manifesta-
tions (Commission on Epidemi ology and Prognosis of
the International League Against Epilepsy, 1993).
Although these are sometimes referred to as the
‘‘epilepsies,’’ we use the term epilepsy throughout this
document. In epidemiologic studies of epilepsy con-
ducted in both developed and low and middle income
countries, different definitions and methods complicate
the tasks of study interpretation and comparison. The
purpose of this document is to promote consistency in
methods and definitions in an effort to enhance future
population-based epidemiologic studies, facilitate com-
parison between populations, and encourage the collec-
tion of data useful for the promotion of public health in
diverse settings. At the same time, this guidance is
intended to be flexible, reco gnizing that public health
infrastructure, needs, and priorities vary among coun-
tries, especially when comparing developing and devel-
oped regions.
Epidemiologic Methods for the
Study of Epilepsy
Surveillance and epidemiologic studies
Public health surveillance is defined as ‘‘the ongoi ng
systematic collection, analysis, and interpretation of
health data necessary for designing, implementing, and
evaluating public health prevention programs’’ (Guide-
lines Working Group, 2001). Examples of public health
surveillance include department of health or ministry sys-
tems that monitor the incidence of diseases, including
communicable diseases, as well as national mortality reg-
istries and systems relying on administrative data such as
hospital discharge (separation) data. Only limited clinical
details are usually collected by such systems, which in
turn limits diagnostic precision and precludes more than
rudimentary classification. As a standard public health
practice, surveillance is usually distinguished from
epidemiologic research, which typically involves col-
lecting more extensive data over a limited period and
often includes analyses to test novel hypotheses regarding
specific questions of etiology or association. The
distinctions between surveillance and research are not
A major purpose of epidemiologic studies and surveil-
lance is to provide the information necessary for primary
prevention, for early detection and treatment, for setting
public health and health care pri orities, and for identifying
other education and service needs associated with health
conditions. To assess the public health importance of epi-
lepsy and to design and promote effective health care and
service programs it is necessary to describe:
the magnitude of the problem (e.g., total number of
cases; incidence rate, i.e., the rate of new cases occur-
ring in the population; mortality rate, i.e., the rate of
deaths occurring with the condi tion; and prevalence,
i.e., the proportion of the population with the condition)
populations at highest risk of epilepsy (e.g., demo-
graphic characteristics)
associations, risk factors, and causes
severity and outcome (e.g., seizure frequency and dura-
tion, symptoms, comorbidities, resulting disability, and
cost of care)
Surveillance is also important to evaluate and monitor
the effectiveness of health care programs, including trends
in these measures over time. To serv e all these purposes, it
is important that epidemiologic and surveillance data be
comparable over time and between locales. Therefore,
inclusion criteria based on standard case definitions are
important. Likewise, it is desirable to collect comparable
data elements as described below.
Attributes of epidemiologic studies and surveillance
A number of attributes of epidemiologic studies and
surveillance determine their success. The following are
among the most important: (Guidelines Working Group
Economy. Economical methods reduce the costs and
time required to collect and analyze data . Successful
Epilepsia, 52(Suppl. 7):2–26, 2011
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Standards for Epidemiologic Studies and Surveillance of Epilepsy
Page 2
studies and systems avoid collecting unnecessary data
and rely as much as possible on data from existing data
collection systems, when of high quality and appropri-
Acceptability. Data collection may require the accep-
tance and cooperation of many persons and organi-
zations involved in reporting cases. Subjects may be
asked to provide substantial time taking surveys and
participating in other assessments. Successful studies
and systems do not place costly, difficult, or unac-
ceptable burdens on those who provide data.
Accuracy. The closeness of the measurement to the true
value is critical. Accuracy of measurements of inci-
dence or prevalence can be characterized by the quali-
ties of sensitivity, specificity, and positive predictive
value (PPV) of the tools to collect the data, which are
described later in this paper.
Representativeness . Representative epidemiologic
studies and surveillance systems include subject s whose
characteristics and experience are simi lar to the popula-
tion of interest. They accurately measure and describe
the occurrence of epilepsy over time. Representative-
ness is critical if data are gathered in only a sample of
epilepsy cases.
Data sources
The data needed for epidemiologic studies of epilepsy
include items necessary for case ascertainment and verifi-
cation, as well as items that help describe other character-
istics of each case defined in later sections of this
document. Th e methods chosen for such epidemiologic
studies depend on the availability and content of different
sources of data. Common sources include:
Direct population surveys. Surveys conducting direct in-
person interviews in households may attempt to contact:
(1) all members of a smaller community (door-to-door
surveys) (Haerer et al., 1986; Osun tokun et al., 1987;
Aziz et al., 1994; Gourie-Devi et al., 1996; Karaagac
et al., 1999; Nicoletti et al., 1999; Tran et al., 2006) or (2)
a systematic representative sample of households (Cen-
ters for Disease Control and Prevention 1994; Wiebe
et al., 1999). Such surveys are especially valuable in
communities where many househ olds lack tele-
phonesas in some low and middle income coun-
triesbut are more time-consuming for interviewers. In
other communities with nearly universal household tele-
phone availability, it may be more efficient to conduct
most surveys by telephone in a repr esentative sample of
households. With the recent proliferation of personal and
mobile telephones, as well as increased screening of calls
by potential respondents, rates of participation in tele-
phone surveys have diminished substantially. Whether
surveys are conducted in person or by telepho ne, limita-
tions on the validity and clinical detail from self-reports
and proxy reports must be considered (Pal et al., 1998).
s If resources permit, some limitations of self- or
proxy-reported data may be overcome if the initial
survey is used to screen for potential cases of epi-
lepsy and if persons suspected of having epilepsy are
subsequently evaluated in person by an epilepsy cli-
nician for confirmation of the diagnosis and to collect
additional information. Many population-based stud-
ies of epilepsy rely on such two-stage methods.
Existing coded data. The International Classification of
Diseases, Ninth Revision (ICD-9) (World Health Orga-
nization 1977) or Tenth Revision (ICD-10) (World
Health Organization 2005), are used throughout the
world to code death certificates for mortality registries.
In addition, these classificationsor a variation like the
U.S. International Classification of Diseases, Ninth
Revision, Clinical Modification (ICD-9-C M) (U.S.
Department of Health and Human Services 2003)are
widely used to code other medical records describing
hospital admissions, or emergency department, clinic,
or physician visits. In some areas, especially in devel-
oped countries, these coded data are collected for the
population and maintained in computerized databases.
Coding processes are prone to error, depending on the
knowledge of the coders, accuracy of code transcrip-
tions, and the accuracy and completeness of the original
clinical records on which coding is based.
s Some countries with integrated universal health care
systems have designed national or regional registers
that can be used as sources for epidemiologic studies
of epilepsy, for example, the Swedish Hospital Dis-
charge Register and the Danish National Hospital
Register (Nilsson et al., 1997, 1999; Adelow et al.,
2006; Sun et al., 2006; Vestergaard et al., 2006).
These systems can be used to identify study popula-
tions, and some may provide sufficient data to
describe important characteristics of the population
with epilepsy. In some countries or districts, it is pos-
sible to obtain population-based hospital discharge
and other health care data that are being collected for
administrative purposes. It should be noted that the
validity and precision of these coded data may be
limited, since a more general code, or the code that
maximizes reimbursement, may be used in prefer-
ence to the most accurat e code.
s In some countries with universal health care systems,
it is possible to link administrative data for an indi-
viduals health care from hospitals, clinics, and phy-
sician offices. An example based on such data is the
Canadian Chronic Disease Surveillance System
(James et al., 2004; Dai et al., 2010). Although it
lacks a national register, the U.S. National Center for
Health Statistics does collect repr esentative samples
of coded health care data in its National Health Care
Survey, which includes separate surveys of a national
sample of hospitals, emergency departments, clinics,
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D. J. Thurma n et al.
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and physician offices. Individual patient care data
across these surveys cannot be linked.
If coded records for all individual patients in a popula-
tion can be linked to describe all inpatient and outpatient
medical encounters over an extended time, then it
becomes possible to ascertain most cases of epilepsy in
that population and to estimate prevalence and incidence
(Holden et al., 2005b). Heretofore such methods have not
been commonly used; their sensitivity, specificity, and
positive predictive values may be limited, and until the
quality and completeness of coded data have been widely
assured, an evaluation of the validity of these linked data
is advised in each population studied (Nilsson et al., 1997;
Christensen et al., 2005; Holden et al., 2005b; Parko &
Thurman, 2009; Jette et al., 2010).
Linking additional coded data that describe prescrip-
tions for antiepileptic drugs (AEDs) can be used to
strengthen the sensitivity and diagnostic accuracy of
coded data (Hauser et al., 1993; Holden et al., 2005b). This
method can also be used for ascert aining putative cases of
active epilepsy in epidemiologic studies (Olafsson & Ha-
user, 19 99; Artama et al., 2005, 2006), but it has limited
application if the treatment gap is large.
Existing uncoded data. Medical records from hospitals,
emergency departments, clinics, and physician offices
(and on occasion, coroner or medical examiner reports)
are the source material for coded data described above
and contain much more information. They are highly
useful both to validate (confirm diagnosis) and to sup-
plement (obtain more clinical and other details) the
information obtained from coded data. The process of
locating, reviewing, and abstracting such records can be
time-consuming, and is sometimes attempted on only a
representative (e.g., rando m) sample of medical
The sensitivity of case ascertainment in population-
based studies of epilepsy can be improved by using multi-
ple sources of information. For example, direct survey
methods may be enhanced by also consulting key infor-
mants who are likely to be aware of persons with epilepsy
in the community, for example, teachers, health workers,
religious leaders, and traditi onal healers. Methods relying
on coded data as a screening tool may be enhanced by also
searching records from neurology and epilepsy clinics and
practices, electroencephalography (EEG) laboratories, or
pharmacy prescription records. Collecting information
from multiple sources of cours e requires measures to con-
solidate duplicate reports and to confirm diagnoses from
less dependable sourcesa challenging task.
Concealment of epilepsy due to negative attitudes about
epilepsy can affect ascertainment in some communities.
Additional problems affecting case ascertainment include
cognitive impairment. Several problems in case ascertain-
ment are compounded in resource poor communities.
These may include poor recall of age of onset of seizures,
poor recall of date of last seizure due to lack of use of cal-
endars, limited detection of nonconvulsive seizures, and
sometimes the absence of a clear terminology for epilepsy
and seizures. Each of these factors may interfere with
ascertainment of epilepsy.
It is essential to understand the significance of seizures
and epilepsy in a specific cultural setting before an epi-
demiologic study is initiated. In particular, the language
and concepts used to describe seizures have a profound
influence on the reliability of questionnaires and screening
methods to detect seizures and epilepsy, recognizing that
many cultures do not have a single term that describes epi-
lepsy, seizures generally, or seizure types.
Definitions of epilepsy and epileptic seizures
The Int ernational League Against Epilepsy (ILAE) has
proposed both conceptual and operational definitions of
epilepsy. In 2005 the following definition of epilepsy was
proposed: ‘‘a disorder characterized by an enduring pre-
disposition to generate epilept ic seizures and by neurobio-
logic, cognitive, psychological and social consequences
of this condition. The definition requires the occurrence of
at least one epileptic seizure’’ (Fisher et al., 2005). This is
a conceptual definition, intended primarily for clinicians
who are diagnosing epilepsy. Epidemiologic researchers
require operational case definitions based on the concep-
tual definition. For the purpose of conducting most popu-
lation-based studies of epilepsy epidemiology, we advise
that epilepsy be defined in practice as two or more unpro-
voked seizures occurring at least 24 h apart. This opera-
tional defini tion is unchanged from that adopted in 1993
by the ILAE (Commission on Epidemiology and Progno-
sis of the International League Against Epilepsy (1993).
This approach has the additional advantage of permitting
comparison of epidemiologic studies across time periods.
Evidence of recurrence may be the only information
available to most epid emiologic studies with which to
identify the presence of an ‘‘enduring predisposition to
seizures.’’ Therefore, there may be no alternative to this
operational definition for most epidemiologic studies.
Exceptions may be considered if other very strong predic-
tors of unprovoked seizure occurrence are identified as,
for example, in studies of genetic etiologies of epilepsy.
The definition of epilepsy requires further definition of
an epileptic seizure. Based on current ILAE definitions we
propose that an epileptic seizure be defined in principle as
‘‘a transient occur rence of signs and/or symptoms due to
abnormal excessive or synchr onous neuronal activity in
the brain’’ (Fisher et al., 2005). Operationally, these signs
or symptoms include sudden and transitory abnormal phe-
nomena such as alterations of consciousness, or involun-
tary motor, sensory, autonomic, or psychic events
perceived by the patient or an observer (Commission on
Epidemiology and Prognosis of the International League
Against Epilepsy 1993). Active epilepsy indicates a
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person who is either currently being treated for epilepsy or
whose most recent seizure has occurred within a time
interval usually defined as the past 2 or 5 years (Commis-
sion on Epidemiology and Prognosis of the International
League Against Epilepsy 1993), but the time should be
specified. In some localities, active epilepsy may be
defined over a 1-year period, due to problems in recalling
dates beyond that period (Longe & Osuntokun, 1989; Bir-
beck & Kalichi, 2004; Mungala-Odera et al., 2004;
Edwards et al., 2008).
Epileptic seizures, even if recurrent, are not always syn-
onymous with epilepsy per se. Traditionally, and based on
strong epidemiologic data supporting the disti nctions, cer-
tain conditions under which seizures occur are generally
not considered to be epilepsy proper, although they are
still considered within a broader spectrum of seizure-
related disorders: (Commission on Classification and Ter-
minology of the International League Against Epilepsy
1989, Engel, 2006a)
Solitary unprovoked epileptic seizures (or a single clus-
ter occurring within a 24-h period or a single episode of
status epilepticus) (Commission on Epidemiology and
Prognosis of the International League Against Epilepsy,
Febrile seizures, occurring in infants and young chil-
dren with a rectal temperature of at least 101 Fahren-
heit (38.3C) in the absence of a history of unprovoked
seizures or concurrent central nervous system infection
(American Academy of Pediatrics Committee on Infec-
tious Diseases, 1987);
Neonatal seizures occurring in infants <28 days (Com-
mission on Ep idemiology and Prognosis of the Interna-
tional League Against Epilepsy, 1993);
Seizures ‘‘in close temporal association with an acute
systemic, metabolic, or toxic insult or in association
with an acute central nervous system (CNS) insult
(infection, stroke, cranial trauma, intracerebral hemor-
rhage, or acute alcohol intoxication or withdrawal),’’
(Commission on Epidemiology and Prognosis of the
International League Against Epilepsy, 1993) that is,
seizures not necessarily due to an established and
‘‘enduring alteration in the brain’’ (Fisher et al., 2005).
Seizures due to such acute and transient conditions have
also been described as provoked or acute symptomatic
epileptic seizures (Commission on Epidemiology and
Prognosis of the International League Against Epilepsy,
1993; Beghi et al., 2010). In these cases, the interval
between the insult and the seizurewhich can be used
to separate acute symptomatic from unprovoked sei-
zuresmay vary according to the underlying clinical
condition (see Appendix, Table A1).
There are exceptional clinical circumstances in which
these criteria may not necessarily exclude a diagnosis of
epilepsy. With increased understanding of the underlying
genetic causes of some of these disorders, the criteria for
exclusion may change in the future. However, at the
current time and for the purposes of most epidemiologic
studies, febrile seizures, neonatal seizures, solitary un-
provoked seizures (or an isolated episode of status epilep-
ticus), and provoked seizures should be segregated from
These definitions of epilepsy and epileptic seizures
often must be translated into more practical criteria that
can be used in epidemiologic studies of epilepsy, given
the types and quality of data that may be available. In this
process, further consideration must be given to the quality
of evidence for a diagnosis of epilepsy: (a) clear evidence
of recurrent epilepti c seizures, with evidence that these
are unprovoked by any acute medical condition or tran-
sient brain disorder; and (b) documentation of diagnosis by
someone with appropriate specialized training in the rec-
ognition of epilepsy. Because the sources of information
available for epidemiologic studies of epilepsy may fall
short of either of these criteria, varying degrees of cer-
tainty can be recognized in a case definition. These cate-
gories are:
Definite, with primary documentation that meets crite-
rion (a) or (b) above.
Probable, with other sources of information indicating
the likelihood that criterion (a) or (b) is met.
Suspect, where primary or other sources of information
suggest a possibility of epilepsy but neither criterion (a)
nor (b) is met. The information provided is inadequate
to confirm or refute the diagnosis of epilepsy.
This classification needs validation. In the Appendix of
this document, operational case defini tions for various
sources of information (e.g., med ical records, coded
health care data, and interview data) are proposed with
varying levels of probability.
Classifications of seizure type and syndrome
Epilepsy encompasses many different conditions with
varying manifestations. Depending on the type and quality
of information available for an epidemiologic study, there
are likely to be limitations in the exte nt to which a study
can classify cases of epilepsy with regard to seizure types
and other characteristics.
In 2010, a revised terminology and concepts for classi-
fying seizures and epilepsies was proposed by the Com-
mission on Classification and Terminology of the ILAE
(Berg et al., 2010). This revision recommended that the
classification be grouped within categories of ‘‘electro-
clinical syndromes,’’ ‘‘constellations,’’ ‘‘epilepsies associ-
ated with structural or metabo lic conditions,’’ and
‘‘epilepsies of unknown cause’’ (Berg et al., 2010). The
classification of individual types within these categories is
based on characteristics including ‘‘age at onset, cognitive
and developmental antecedents and consequences, motor
and sensory examinations, EEG features, provoking or
triggering factors, and patterns of seizure occurrence with
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D. J. Thurma n et al.
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respect to sleep’’ (Berg et al., 2010). If highly detailed
information from epilepsy specialists is consistently avail-
able in an epidemiologic study, the ILAE classifications
of seizures and epilepsy syndromes may be considered
(Commission on Classification and Terminology of the
International League Against Epilepsy 1981, 1989; Engel,
2006b; Berg et al., 2010).
Although accurate identification of the type of epilepsy
is important for treatment and management, epidemio-
logic studies may not achieve such precision because:
(1) investigators lack access to clinical records, (2) inves-
tigators lack the knowledge and training to make specific
epilepsy diagnoses, or (3) patients lack access to the level
of specialty care needed for specific diagnosis. In the
absence of detailed, consistently collected classification
information, an alternative is to collect more basic infor-
mation elements. At a minimum, these include the age at
onset, the typical manifestations (seizure types), and the
underlying cause (etiology) as outlined below.
Age at onset
Defined as the age of occurrence of the first unprovoked
seizure, age at onset is especially important to studies of
epilepsy, as many specific syndromes have a characteristic
age at onset useful to their recognition. The accuracy of
pinpointing the age of onset varies by the patients seizure
types, as some types (e.g., absence, myoclonic, complex
partial) may recur over prolonged periods before being
diagnosed as seizures (Jallon et al., 2001).
Seizure types
The ILAE seizure classifications (Commission on Clas-
sification and Terminology of the International League
Against Epilepsy 1981, 1989; Engel, 2006b; Berg et al.,
2010) require clinical expertise and evaluations that may
not be available to people with epilepsy in some localities.
Furthermore, if done, the results of such evaluations are
often not available to epidemiologic researchers. Under
these circumstances, the only available information may
be descriptions from the person with epilepsy or from a
witness. The seizure classification must then be simpli-
fied, based on limited characteristics of the ictus such as
the presence or absence of:
focal sensory or motor symp toms or signs,
major convulsive (e.g., generalized tonic–clonic) activ-
other types of motor activity, and
impaired responsiveness or consciousness during the
To enable comparisons among past and future studies it
is important that this simplified classification relate as clo-
sely as possible to more detailed categories and concepts
Table 1. Simplified clinical classification of seizure type
Generalized Focal Undetermined
Predominantly motor
Convulsive Generalized convulsive
Focal onset with secondary generalization
Convulsive undetermined
Other motor Generalized other motor
Focal motor
Other motor undetermined
Predominantly nonmotor
Impaired responsiveness
Generalized absence
Dyscognitive focal seizures (formerly
complex partial)
Impaired responsiveness,
Other nonmotor NA Sensory, psychic, and other, including
Generalized seizure, unspecified Focal seizure, unspecified Seizure, unspecified
Seizure onset is manifested by generalized tonic and/or clonic (convulsive) motor activity and unconsciousness. Focal features
may occur.
Seizure onset has focal manifestations that evolve to generalized convulsive activity.
Focal or generalized nature of seizure onset is undetermined, but seizures manifest generalized convulsive activity.
Include myoclonic seizures, eyelid myoclonus, epileptic spasms, atonic seizures, other, and unspecified generalized motor sei-
zures with or without impairment of consciousness.
Seizure has focal manifestations (including myoclonic, inhibitory, Jacksonian march, focal asymmetric tonic, hemiclonic, hyperki-
netic, and other focal motor seizures) that do not evolve to generalized convulsive activity.
Unspecified motor seizures; includes neonatal and other seizures.
Staring spells, unresponsiveness, or other alteration of consciousness.
Includes typical and atypical absence seizures.
Focal seizure associated with impairment of consciousness (formerly termed ‘‘complex partial’’) without secondary generaliza-
tion (Commission on Classification and Terminology of the International League Against Epilepsy, 1989).
Seizure manifested by transient decreased responsiveness or ‘‘staring,’’ undetermined if absence or dyscognitive (‘‘complex par-
tial’’) in type.
Includes auras without alteration of consciousness or secondary generalization (including somatosensory and experiential sei-
zures), autonomic, and other nonmotor seizures.
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of current ILAE classifications and terminologies (Berg
et al., 2010; Blume et al., 2001; Commission on Classifi-
cation and Terminology of the International League
Against Epilepsy 1981; Engel, 2006b; International Lea-
gue Against Epilepsy (ILAE) (2008). To this end, we sug-
gest that most seizures can be classified within the
following matrix (
Table 1). The matrix classifies seizures
both according to onset (generalized or focal) and predom-
inant ictal features (motor vs. nonmotor), and its catego-
ries allow for varying levels of available information and
certainty regarding seizure type. EEG is not necessar ily
required for this classificationrecognizing that it is often
unavailable in epidemiological studiesalthough it can
be critical in correct diagnosis and may be an important
indicator of underlying focal abnorma lities, particularly in
low and middle income countries (Munyoki et al., 2010 ).
Etiology (proximate cause)
New terminology and concepts advanced by the ILAE
divide the causes of epilepsy into three broad cat egories:
genetic, structural/metabolic, and unknown (Berg et al.,
2010). Although these categories correspond roughly to
the former categories of idiopathic, symptomatic, and
cryptogenic, there are important conceptual distinctions.
The cause of epilepsy is considered genetic when epi-
lepsy is a direct result and the core manifestationof
a known or presumed genetic defect.
The cause is considered structural/metabolic when a
structural lesion (either static or progressive) or meta-
bolic condition (e.g., inborn errors of metabolism) is
present and is known to be associated with an increased
risk of epilepsy. When such a lesion or condition arises
due to a genetic defect, that is, when ‘‘there is a separate
disorder interposed between the genetic defect and the
epilepsy,’’ the cause of epilepsy should be classified as
structural/metabolic. Therefore, the cause is attributed
to the condition that is most directly linked and proxi-
mate to the development of epilepsy.
If the nature of the cause is not known, then such cause
is classified as unknown.
Within each of these three broad categories are many
heterogeneous specific causes. We suggest that the sub-
categories in
Table 2 may provide a useful basis for
epidemiologic studies of epilepsy etiologies. Previous
epidemiologic studies have classi fied etiology in the broad
categories of idiopathic/cryptogenic and symptomatic,
with separate subcategories assigned to the latter. Under
the new terminology, an analogous categorization that
would permit comparisons with previous studies com-
bines genetic, presumed genetic, and unknown and sepa-
rately classifies structural/metabolic causes. Although no
single classification scheme of causes will satisfy all the
purposes among epidemiologic studies, this broadly
organized structure may provide flexibility to allow
Table 2. Classification of epilepsy causes
Direct etiology
Genetic/Presumed genetic Structural/Metabolic
Unknown or
Specific genetic epilepsy syndromes
Genetic and chromosomal developmental
Traumatic brain injury
Mesial temporal sclerosis
Degenerative neurologic diseases
Metabolic or toxic insults to brain
Perinatal insults
Intraventricular hemorrhage
Hypoxic–ischemic encephalopathy
Malformations of cortical or other brain development
Neurocutaneous syndromes
Inborn errors of metabolism
Epilepsy of unknown
Epilepsy of undetermined
Without known etiology despite adequate evaluation (e.g., history, examination, EEG, and other testing determined to be rele-
vant such as neuroimaging or genetic testing).
Where evidence is lacking that the structural pathology precedes the onset of epilepsy, it is not assumed that such pathology
causes epilepsy.
With epilepsy as a late effect. For distinction with acute symptomatic seizures, see page 6.
Includes conditions where underlying etiology is undocumented or available information is limited to terms such as ‘‘intellectual
disability’’/’’mental retardation’’ or ‘‘cerebral palsy’’ when these preceded the onset of seizures.
Without adequate evaluation to determine etiology as defined by investigators.
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modifications (and more detail) according to the focus of
the study as well as to accommodate advancements in
understanding causes. The differentiation between these
categories will depend upon the available facilities [e.g.,
EEG and magnetic resonance imaging (MRI) may be
available, but not access to selected metabolic tests and
genotyping]. Validation of this categorization is needed.
In population studies, comorbiditythe co-occurrence
of two or more separate medical conditions in the same
individualis of greatest interest when it appears at
above-chance levels, that is, when it is associated. Several
comorbid conditions associated with epilepsy have been
described (Sunder, 1997; Hermann et al., 2000; Boro &
Haut, 2003; Trinka, 2003; Gaitatzis et al., 2004; McLach-
lan, 2006; De Simone et al., 2007) and are listed in Appen-
dix, Table A2. Associations between epilepsy and other
medical and psychiatric conditions may exist because
epilepsy and the comorbid condition share an underlying
etiology or because epilepsy or epilepsy treatment lead to
a higher than expected occurrence of the comorbid condi-
tion. It is also possible that epilepsy comorbidities present
at the time of the incident diagnosis may influence
prognosis of epilepsy.
Although not validated in specific studies of epilepsy,
comorbidity indices (Linn et al., 1968; Charlson et al.,
1987; Deyo et al., 1992; Miller et al., 1992; Dodds et al.,
1993; Elixhauser et al., 1998; Selim et al., 2004) can be
used to assess the impact of comorbid conditions on the
prognosis of epilepsy. Such indices may assess the num-
ber, type, and severity of other conditions, regardless of
whether they are associated with epilepsy. These indices
are most often coded according to information from medi-
cal records and have been mos t often used in studies of
mortality (Kwon et al., 2011). Other assessments of com-
orbidity burden have relied on patient self-report, and sev-
eral studies have shown that these questionnaires are
reliable and valid in adult populations (Commission on
Classification and Terminology of the International Lea-
gue Against Epilepsy , 1989; Harlow & Linet, 1989; Linet
et al., 1989; Katz et al., 1996; Bergmann et al., 1998a,b;
Selim et al., 2004). In some circumstances (e.g., psychiat-
ric disorders, migraine), it may be useful to use validated
Epilepsy care
Access to care
To describe fully the determinants of access to care it is
useful to employ a behavioral model of health service use
that identifies predisposing factors (including demograph-
ics and health beliefs), enabling factors (such as income
and health care availability), and need factors (such as dis-
ease severity) (Andersen, 1995). Based on the behavi oral
framework, access to care is measured by examining the
association between patterns of healthcare use and predis-
posing and enabling factors while controlling for compa-
rable need. At a more basic level, a number of studies,
particularly in low and middle income countries (LMIC),
address access to care with regard to the treatment gap,
the difference between the number of people with active
epilepsy and the number being appropriately treated
(Meinardi, et al. 2001; Mbuba et al., 2008). The treatment
gap measures alone do not provide information about
underlying factors that limit access to care (
Table 3). The
treatment gap has traditionally referred to the use and pro-
vision of antiepileptic drugs (AEDs) where use of AEDs is
usually based upon self-reporting. This may have low sen-
sitivity and specificity when compared to the detection of
AED levels in the blood (Edwards et al., 2008).
Some studies of access to care among people with epi-
lepsy have been conducted in populations of developed
countries, emphasizing access to specialty care (Bhatt
et al., 2005; Burneo et al., 2005a; Gaitatzis et al., 2002).
Patterns of health service use and associated costs can be
addressed using both patient-based and population-based
approaches (Halpern et al., 2000). Direct population sur-
veys (or representative samples)including standardized
questions regarding type and quantity of treatmentsare
preferred methods of measuring treatment gaps. Indirect
methods can, howe ver, be employed when data are scarce,
for example, comparing estimates of epilepsy prevalence
from othe r sources to the number of people known (or esti-
mated) to be in treatment. Such estimates are subject to
Table 3. Potential causes of the treatment
gap for epilepsy
Primary (diagnostic gap) Secondary (therapeutic gap)
Lack of adequate paraclinical
services (Diop et al., 2003)
Lack of qualified medical
personnel (Diop et al., 2003)
Lack of access to health care
(distance or cost)
(Tran et al., 2008)
Error in diagnosis
(Kanner, 2008)
Patient and family rejection of
diagnosis due to stigma
(Muela Ribera et al., 2009;
Rafael et al., 2010)
Patient misconceptions about
the nature of condition
(Shibre et al., 2009)
Lack of treatment availability
(Odermatt et al., 2007)
Inability to pay for drugs
(Mac et al., 2007)
Low quality of drugs
(Mac et al., 2008)
Error in treatment prescribed
(Feely, 1999); Rejection of
treatment by patient
(Broadley, 2004)
Cultural beliefs (Meinardi,
et al. 2001)
Uninformed choices or poor
understanding of the nature
of treatment by patient or
family (Stores, 1987; Jacoby,
Poor compliance of
alternative medicine
(Tandon et al., 2002;
Ricotti & Delanty, 2006)
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numerous biases and should only be used as broad indica-
tors of access to care.
Cost studies
Cost-of-illness studies are important to determine the
burden of epilepsy on individuals and society (Begley &
Beghi, 2002). Epilepsy-specific costs are defined as the
economic value of services consum ed in the prevention,
treatment, or rehabilitation of people with the disorder
(direct cost s), and the estimated economic value of work
and leisure activity lost from associated morbidity and
mortality (indirect costs). The cost of services is usually
estimated according to the average payment made to ser-
vice providers. Indirect costs are typically estimated in
terms of lost earnings and the imputed value of lost house-
hold work associated with morbidity and mortality. Two
broad approaches (the bottom-up and the top-down) are
used in gener ating estimates of the direct costs of epilepsy.
In the former, cost estimates of health care, social services,
and family member resource s used by patients with epi-
lepsy are derived from studies (Gaitatzis et al., 2002) of
real cases or, alternatively, based on hypothetical informa-
tion from expert panels and related literature sources. In
the latter, estimates are made of the cost of services for all
illnesses (defined by disease-specific codes) based on
nationally represe ntative provider surveys, and a portion
is attributed to epilepsy. In general, the top-down
approach cannot be used in resource-poor countries due to
inadequate information and differing measures of cost.
Cost-effectiveness studies involve the comparative
assessment of alternative courses of action in terms of
their relative cost and effectiveness (Heaney & Begley,
2002). A cost-effectiveness ratio is calculated for each
treatment or service being assessed, where the denomina-
tor reflects the incremental gain in health (e.g., reduction
in seizure frequency or severity) and the numerator
reflects the additional cost of achieving that health gain.
When the gain in health is expressed in terms of health-
related quality of life, such as Quality Adjusted Life Years
(QALYs) or Healthy Year Equivalents (HYEs), the eco-
nomic assessment study is described as cost-utility analy-
sis (Langfitt et al., 2006).
In cost-benefit analyses the denominator (e.g., resultant
health state) is expressed in monetary terms, and interven-
tions are recommended if the monetary value of the resul-
tant health state exceeds that of the intervention. Because
these analyses entail substantial complexity and potential
ethical concerns, they are performed infrequently (Johan-
nesson & Jonsson, 1991). An alternative form of eco-
nomic evaluation is cost-minimization analysis in which
assumptions are made that treatments produce identical
health benefits. The aim here is to determine the relative
cost of each treatment (Heaney & Begley, 2002).
Because precise data on resource utilization and treat-
ment effects are generally lacking, most studies are con-
ducted for hypothetical cohorts of patients based on data
derived from clinical trials and expert panels (Heaney &
Begley, 2002).
Disparities can occur in epilepsy incidence, prevalence,
access to care, treatment choices and response, complica-
tions, and comorbid conditions (Gakidou et al., 2000;
Braveman, 2006). The topic is of concern to advocates
and policy makers seeking to eliminate inequalities and
improve health in high-risk populations. Disparities may
exist in several dimensions: socioeconomic status (SES);
education; race or ethnicity; geography; physical and
social environment; and others. These factors, particula rly
SES, may covary with the other factors. Some categories,
for example, race or ethnicity, represent social constructs
that are difficult or impossible to mea sure objectively or
Socioeconomic disparities in epilepsy
The concept of socioeconomic status is complex, relat-
ing to material and social assets and resources as well as
social prestige (Krieger et al., 1997). SES can be assessed
for individuals, households, or communities (Krieger
et al., 1997). Several standardized indices of SES that
incorporate elements such as educational attainment,
occupation, and income have been employed and validated
in developed countries (Duncan, 1961; Hollingshead,
1975; Blishen et al., 1987; Nakao & Treas, 1992; Cirino
et al., 2002). Simpler scales emphasizing resource access
and item ownership may be of greater use in low and mid-
dle income countries (Patel et al., 2007). Where studies of
epilepsy obtain information from subject interviews,
standardized SES assessment tools may be considered;
however, many may be too lengthy to be practical. Alter-
natives in developed countries are to estimate SES catego-
ries employing community-based methods, such as the
use of census tract or postal codes, when the average SES
of people in the district is known, recognizing that some
misclassification of individuals and households is likely
with these methods (Krieger et al., 2002). Briefer individ-
ualized methods for assigning SES categories include
using limited information about occupation, education,
income, or property ownership to the extent that these can
be obtained from medical charts or from interviews. The
relationship of SES to epilepsy incidence, prevalence,
healthcare use, and outcomes deserves further study in
both developed and LMIC; among the latter, average SES
may be considerably lower, but within-population varia-
tion greater.
Ethnic/racial disparities
A number of studies have identified disparities in epi-
lepsy occurrence, healthcare use, and outcomes among
categories of race or ethnicity (Szaflarski et al., 2006a).
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Assigning study subjects to racial and ethnic categories
confronts ambiguities, especially with frequent intergroup
marriage and affiliation. In the absence of objective meth-
ods to assign categories, studies may use self-designa-
tions, rely on designations recorded in medical records,
or even impute values based on family name, language,
or geographic information (Fiscella & Fremont, 2006).
Limitations of such methods and their potential for
misclassification must b e recognized, and these may be
superseded by more detailed genetic studies.
Other dimensions and manifestations of disparity
Age, sex, size of community, cultural beliefs and prac-
tices, religion, type of health care system, and variations
in health knowledge and attitudes toward epilepsy are
other factors that may influence disparities in epilepsy
occurrence, care, or outcome and, therefore, merit study.
Numerous studies have identified disparities in epilepsy
in North Amer ica (Szaflarski, et al. 2006b; Theodore
et al., 2006; Burneo, et al. 2009) and elsewhere. With
some, there is a large potential for confounding among the
factors they examine. In order to isolate the underlying
factors of most importance, great care is required in the
selection of diverse study populations for comparison, the
collection of data, and their analysis.
Epilepsy severity and outcomes
The overall impact of epilepsy on health and well being
depends on many factors, beginning with the types of sei-
zures experienced, seizure frequency, underlying neuro-
logic conditions, other comorbidities, treatment side
effects, and the extent to which these are mitigated or
resolved with medical treatment over time. Clinical
parameters are not sufficient to evaluate the effect of epi-
lepsy on the health of the single patient. Other factors also
contribute to the outcomes of epilepsy: in particular,
whether familial and cultural attitudes towar d epilepsy are
stigmatizing or supportive, and whether families and soci-
ety provide educational, vocational, and other resources
that enable people with epilepsy to circumvent potential
disabilities they might encounter. It is of value for epidem-
iologic studies of epilepsy to address these factors, to col-
lect data regarding seizure type and frequency, care
received, and basic indicators of outcome such as social
integration, educational and vocational attainment,
employment status, and perceived quality of life.
Table 5. Examples of validated health-related quality of life assessment instruments
No. items References
Generic measures
SF-36 36 Brazier et al. (1992), Ware & Sherbourne (1992), Weinberger et al. (1991)
SF-12 12 Jenkinson & Layte (1997), Kazis et al. (2006), Ware et al. (1996)
HRQOL-14 14 Centers for Disease Control and Prevention (2010); Moriarty et al. (2003)
Healthy Days/HRQOL-4 4 Centers for Disease Control and Prevention (2007); Moriarty et al. (2003)
Satisfaction with Life Scale (SWLS) 5 Diener (1984), Larsen et al. (1985), Pavot et al. (1991)
Epilepsy-specific measures
Liverpool Batteries Jacoby et al. (1992)
Quality of Life in Epilepsy Questionnaires
QOLIE-89 89 Devinsky et al. (1995)
QOLIE-31 31 Cramer et al. (1998)
QOLIE-10 10 Cramer et al. (2000)
QOLIE-AD-48 (adolescents) 48 Cramer et al. (1999)
QOLCE (children) 91 Sabaz et al. (2000)
Table 4. Examples of epilepsy-related severity assessment instruments
No. items/(dimensions) References
Seizure severity measures
Seizure Frequency Scoring System (5) Engel et al. (1993)
VA Seizure Frequency and Severity Scale 6–21 Cramer et al. (1983)
National Hospital (Chalfont) Seizure Severity Scale 8 O’Donoghue et al. (1996)
Occupational Hazard Scale (6) Janz (1989)
Liverpool Seizure Severity Scale 16, 20 Baker et al. (1991, 1998b)
Hague Seizure Severity Scale (children) 13 Carpay et al. (1997)
Syndrome Severity Measures
Syndrome Severity Scale Dunn et al. (2004)
Epilepsy Severity Measures
Global Severity of Epilepsy Scale (children) 1 Speechley et al. (2008)
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Standardized measur es of seizure severity have been
developed for clinical trials (
Table 4). These, in general,
take account of variables such as the type, duration, and
frequency of seizures; other ictal phenomena such as falls;
and duration of postictal recovery. They require the col-
lection of clinically detailed information to a degree that
may be impractical for many population-based epidemio-
logic studies. In emphasizing the characteristics of
seizures, they do not measure the total impact of epilepsy.
A full assessment of the severity of epilepsy may require
items based on patient perception of seizure severity, since
observer evaluation does not capture the physical and
emotional burden experienced by the patient (Baker et al.,
1998a). However, the inclusion of a subjective evaluation
may decrease the reliability of the scale. To varying
degrees, both kinds of seizure severity scales have prob-
lems regarding ease of use, sensitivity to changes, and sub-
jectivity of assessments, and no single scale is accepted
generally as a standard. No scale has been developed for
low and middle income countries where other factors such
as physical consequences (e.g., burns) are important.
Their characteristics are compared in detail elsewhere
(Cramer & French, 2001).
A classification of epilepsy syndrome severity for
children has also been proposed (Dunn et al., 2004).
This classification is based on the diagnosis of syndrome
and does not take into account individual differences in
seizure manifestations (e.g., type and frequency) among
persons with a given syndrome. Its usefulness is limited
by the availability of complete and accurate syndrome
diagnosis information. Finally, a simple, physician-rated,
single-item scale to assess global severity of pedi-
atric epilepsy severity has demonstrated reliability and
validity (Speechley et al., 2008) and is being used
in multicenter studies of quality of life in pediatric
Quality of life
Health-related quality of life (HRQoL) can be regarded
as the broadest and most important outcome of any
chronic health condition. The relationship between
chronic health conditions, HRQoL, and disability is inter-
related and complex, and can sometimes pose measure-
ment chal lenges (Krahn et al., 2009). Comprehensive
measures of HRQoL take int o account social, vocational,
cognitive, and mood states, in addition to overall per-
ceived physical and mental health.
Some also address processes of health care or quality of
care (Donabedian, 2005). Preference-based or ‘‘utility-
based’’ measures of HRQoL are used by some to assess
clinical outcomes and for cost-utility studies [e.g., Euro-
QOL (EQ)-5D] (The EuroQol Group 1990).
Assessments of HRQoL can be generic , intended for
use in general populations, and not restricted to assessing
outcomes of a specific disease or disorder. The use of gen-
eric instruments in studies of epilepsy allows comparisons
with other conditions and with the general population
(Langfitt et al., 2006). Other HRQoL measures are dis-
ease-specific, designed to addre ss the physical, psycho-
logical, and social aspects of a particular condition.
Recently, recommendations to avoid conflating function
and HRQoL were provided (Krahn et al., 2009). For epi-
lepsy, various measurement instruments have been devel-
oped or adapted for which reliability, validity, and
sensitivity to change have been demonstrated (
Table 5)
(Leone et al., 2005). These epilepsy-specific measures can
provide insight on the impact of epilepsy care and may be
more likely to identify variations in outcomes of epilepsy
Important considerations in selecting measures of
HRQoL for use in surveys of epilepsy include:
The appropriateness of the instrument to local lan-
guage and cultures. Most of these scales have been
validated primarily in a few developed countries.
Their appropriateness in other regions and the compa-
rability of their results across cultures has not been
fully assessed. At the same time, however, changes in
such scales to adapt them to new cultures are best
minimized in order to maintain their equivalence
across cultures.
The feasibility of implementing the assessment in a sur-
vey or epidemiologic study is determined in part by its
length and mode of administration. Most instruments
addressing quality of life require extensive information
that is not routinely documented in medical records;
thus, they may not be suited for many population-based
studies of epilepsy. Lengthy HRQoL instruments that
are self-administered may be cognitively burdensome
for people with epilepsy, thereby limiting the quality of
the data.
Validation of the instrument with respect to its reliabil-
ity (consistency of results internally and with repeated
administration), construct validity, and criterion valid-
Sensitivity of the instruments measures of effect to
changes in treatment and other determinant circum-
In countries with sufficient technical means, HRQoL
may be more efficiently assessed through new resources
available from the Patient Reported Outcome Measure-
ment Information System (PROMIS) supported by the
U.S. National Institutes of Health (NIH) (Nationa l Insti-
tutes of Health, 2010a,b). By integrating item-response
theory and computer adaptive tes ting, PROMIS has
developed on-line resources at no cost for researchers to
use to measure patient-reported symptoms and HRQoL
across a wide variety of chronic diseases and conditions.
A distinct, but closely related NIH project, Neuro-QOL
(National Institutes of Health, 2010c), will validate item
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banks to measure quality of life specifically in adult and
pediatric populations with various neurologic diseases,
including epilepsy.
Other specific outcomes related to quality of life
It should be noted that seizure frequency alone, an
important element of measuring severity, is also an impor-
tant predictor of HRQoL. In general, those whose seizures
fully remit (with or without ongoing treatment) show little
reduction in HRQoL, whereas those with refractory epi-
lepsy demonstrate substantial decrements in quality-of-
life scores (Devinsky et al., 1999; ODonoghue et al.,
1999; Wiebe et al., 1999; Leidy et al., 2001; Strine et al.,
2005; Kobau et al., 2007).
Other important assessments related to quality of life
that may be considered in population-based studies of epi-
lepsy address specific psychosoci al dimensions such as
depression, anxiety, perceived stigma, cultural beliefs and
attitudes about epilepsy, as well as positive aspects of
functioning (e.g., positive affect and satisfaction with life
Terminal remission
Epilepsy, characterized by recurrent seizures, may con-
tinue for a limited time in some individuals, whereas it can
be a lifetime condition in others. Terminal remission of
epilepsy is defined as a period of 2 or 5 years of seizure
freedom off AEDs (Hauser & Kurland, 1975).
Epilepsy burden on the families
The burden of epilepsy reaches well beyond those with
the condition, affecting other persons of close relationship
defined by family, household, or friendship, as well as
broader relationships in the community. The effects are
strongest on the family or household and will vary depend-
ing on:
family composition and structure and the role of the
person with epilepsy (e.g., parent or caregiver, minor
child, or adult child),
the severity of epilepsy and presence and severity of
comorbid conditions,
the familys socioeconomic and health insurance status,
the familys and communitys perceptions of epilepsy,
and relevant social norms and expectations, and
the support systems available.
Parents with epilepsy may have restricted opportunities
for gainful employment outside the home, or restricted
abilities to perform work as homemakers and primary
caregivers for their children. In general, these limitations
may disproportionately affect women with epileps y.
Parents of children with epilepsy may be taxed by addi-
tional caregiver burdens, especially if seizures are intrac-
table or accompanied by cognitive, behavioral, or
developmental disabilities. Siblings of children with
epilepsy may be affected by relatively reduced parental
attention or by needing to assume some caregiving re-
sponsibilities themselves. Families with members who
have epilepsy may be isolated from the neighborhood and
community because of stigma and discrimination due to
culture and folk traditions. Stigma can be measured with
the Epilepsy Stigma Scale (Baker et al., 2000; Baker,
Although the overall impact of epilepsy on the family
can be profound, there are currently no specific instru-
ments that measur e this burden. The Impact of Pediatric
Epilepsy Scale (IPES) and structured questions about the
burden on siblings have been used to evaluate the influ-
ence of epilepsy on several aspects of family functioning
(Mims, 1997; Camfield et al., 2001; Tsuchie et al., 2006).
The quality of life and psychosocial dimensions (e.g.,
depression, anxiety, stigma, etc.) of family members,
especially care providers, can also reflect the epilepsy bur-
den on the families. Further research is needed in this
Analysis in population studies
Just as consistent definitions, variables, and data collec-
tion methods are important to enable comparisons among
studies, so are consistent analyses and reported measures
important. The common standard measures of the fre-
quency of epilepsy in a population are point prevalence,
incidence, and risk:
Point prevalence is the proportion of individuals in the
population who are affected by a health condition at a sin-
gle point in time, usually designated as a specific day.
With epilepsy, the point prevalence of active epilepsy is
typically considered of most interest.
A case of active epilepsy indicates a person who is
either currently being treated for epilepsy or whose most
recent seizure has occurred (usually) within the past
2–5 years (Commission on Epidemiolo gy and Prognosis
of the International League Against Epilepsy, 1993),
although the past year has been used in low and middle
income countries (Longe & Osuntokun, 1989; Birbeck &
Kalichi, 2004; Mungala-Odera et al., 2004; Edwards
et al., 2008). Variations on this definition of active epi-
lepsy can be used for the purposes of some studies lacking
long-term retrospective data on seizure occurrence; how-
ever, to enhance comparability across studies, variations
in this definition should be minimized, and the time period
must be stated in the report.
Point prevalence is useful for indicating the degree of
disease burden in a population. Prevalence studies are
especially relevant when assessing healthcare and other
service needs. They are less useful for etiologic investiga-
The incidence rate is the rate with which new cases
occur in a population. It is expressed as a frequency per
standard population (e.g., 100,000) per time period (usu-
ally per year). Incidence rates are informative about the
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rates of new cases regardless of the prognosis or cause of
the disorder. For etiologic and prognostic investigations,
incidence-based stud ies are superior to prevalence-based
New onset versus newly diagnosed epilepsy incidence
In practice, incidence studies are often limited to study-
ing the incidence of new diagnosis, since many persons
with newly diagnosed epilepsy have had a long, uncertain,
or undocumented period of seizure occurrence preceding
their diagnosis. The time from onset to diagnosis varies
substantially with the type of epilepsy and its predominant
seizure type (Anonymous, 2000).
The magnitude of the incidence of new onset epilepsy
and the incidence of newly diagnosed epilepsy will differ,
because these measures have different numerators. For
new onset epilepsy, the numerator includes people identi-
fied at their second unprovoked seizure. In contrast, the
numerator for newly diagnosed epilepsy includes both
new onset epilepsy and people with more than two unpro-
voked seizures who are first diagnosed with epilepsy dur-
ing the study period. The incidence of new onset epilepsy
has never been compared to that for newly diagnosed epi-
lepsy, but it is expected that the incidence of new onset
epilepsy will be less than that for newly diagnosed epi-
A variant expression of incidence is incidence density,
which uses a different denominator, population-time (per-
son-time), to describe the accrual of events (new epilepsy
diagnosis) over an interval of time when population is
observed under the assumption of stability. The advantage
of incidence density is it can easily be converted into risk
and is more suitable for rare events such as epilepsy whe n
numerator is stabilized as an accrual of cases over a
specified time interval. Furthermore, it is the best approxi-
mation of cumulative incidence for rare diseases (Morgen-
stern et al., 1980).
The risk of epilepsy is the probability that a person will
develop the disorder. It represents the cumulative effect of
incidence over a longer period of time or span of age, for
example, a 5-year risk, the cumulative risk in childhood,
or a lifetime risk. The cumulative risk over the lifetime
is sometimes called ‘‘lifetime prevalence.’’ In practice,
the last measure can seldom be determined without relying
on retrospective data sources and is thus more prone to
Using administrative health datasets to estimate incidence
or prevalence
Linked hospital, emergency department, clinic, and
physician office data that describe all medical encounters
for all individuals in a population may be used to estimate
epilepsy incidence or prevalence. The specificity and
positive predictive value of diagnostic codes for epilepsy
and seizures must be considered (see Appendix). When
individuals have multiple medical encounters described
by epilepsy or seizure codes, the likelihood of identifying
a true case of epilepsy is high; however , these codes do
not distinguish new-onset from long established cases.
Because some people with established epilepsy seek fol-
low-up medical care infrequently, for example, less than
once yearly, the first appearance of a code for epilepsy
with an individual enrolled in a dataset cannot be assumed
to represent a new diagnosis of epilepsy unless there is a
long preceding period of enrollment with no record of sei-
zure or epilepsy. Ideally for determination of incidence,
there should be no prior code for epilepsy in the adminis-
trative data , particularly when individuals are represented
over all or most of the lifetime. However, in other situa-
tions, this may be impractical. When administrative data
do not cover all or most of the lifetime, the minimum
length of enrollment before the first epilepsy-related code
that may represent a new epilepsy diagnosis is uncertain: 1
or 2 years appears to be inadequate, and at least 3 (or
preferably more) years are advisable. For prevalence
studies, similar periods of follow-up appear necessary to
ascertain nearly all existing cases.
Mortality rates are higher in people with most types of
epilepsy, and this may be attributed both to consequences
of seizure occurrence as well as to direct effects of some
underlying diseases that give rise to epilepsy (Gaitatzis &
Sander, 2004). Rates of sudden unexpected death in peo-
ple with epilepsy (SUDEP) are also greater than rates of
sudden unexpected death in the general population (Tom-
son et al., 2005). Determination of the cause of death may
be useful in identifying measures to prevent deaths in this
Different measures are used to estimat e mortality,
depending upon the study design and available informa-
tion on deaths. The measures include mortality rate, case
fatality, standardized morality ratio, and proportionate
mortality. A standardized mortality ratio (SMR) is the
most common measure used to compare rates of death
between a population of people with epilepsy and a refer-
ent population. The magnitude of one SMR cannot be
compared to the magnitude of another SMR, because the
SMR is calculated using indirect standardization. As a
result, the referent for each SMR has a different age distri-
bution that alters the expected deaths. Mortality rates can
also be used when information on the number of people in
the general population is available. The proportion of
deaths in the epilepsy population can be reported using
case fatality. Sometimes only death data that list cause of
death are available. In this case, it is possible to calculate
the proportionate mortality, which is the ratio of the num-
ber of deaths due to a specific cause in a population to the
total number of deaths in the same period (Logroscino &
Hesdorffer, 2005) describes the proportion of deaths in a
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
D. J. Thurma n et al.
Page 13
community that are due to epilepsy compared to deaths
due to other causes.
Evaluation of measurement validity
The validity of basic measures of incidence, prevalence,
and mortality is described by these proportions:
The proportion of cases of disease detected by the case
ascertainment method. Sensitivity is low if many true
cases of disease are unreported (or misclassified as non-
cases), resulting in too-low estimates of disease occur-
The proportion of the study population without disease
that is correctly classified by the case ascertainment
method represents its specificity. It is reduced if true non-
cases of disease are misclassified as cases (false positives),
which can inflate estimates of disease occurrence.
Positive predictive value (PPV)
The proportion of ascertained cases that actually have
the disease. As with specificity, PPV is also reduced if
true noncases of disease are misclassified as cases, lead-
ing to excessive estimates of occurrence. The PPV var-
ies directly with the prevalence of disease in the
population under study, that is, diminishing as the preva-
lence of the condition decreases. For conditions of lesser
prevalence, however, a high PPV provides greater assur-
ance of accuracy than a specificity measurement of the
same value.
Unlike PPV, sensitivity and specificity are conditional
probabilities predicated on the availability of a definitive
test that should be predetermined and fixed as antecedents.
In othe r words, unless otherwise the marginal totals corre-
sponding to the definitive test, ‘‘gold standard,’’ are fixed
ahead of time, one can find a desirable level of sensitivity
and specificity by manipulating the cell values (Fleiss
et al., 2003).
Measures of association
Measures of frequency are typically compared to each
other to produce measures of association or measures of
effect. Associations are typically quantified as differences
or ratios.
Absolute (difference) measures are calculated as differ-
ences in prevalence, risk, or rate between one group and
another (e.g., men vs. women, one country vs. another,
persons exposed to a risk factor of interest vs. persons not
exposed). Absolute difference measures express the effect
of a risk factor in terms of the actual proportion of people
affected. This proportion, when applied to a specific popu-
lation, can be used to estimate the number of people
Relative (ratio) measures are calculated as the rate
(risk, prevalence) in one group divided by that in a referent
group. This is a preferred method for portraying the
impact of a factor on the individual and quantifying risk
factor–disease associations in etiologic research, because
it provides information on the degree to which the risk of
disease is increased in groups with a factor of interest
compared to those without the factor.
It is often helpful to use absolute and relative measures
of association together, as they provide somewhat differ-
ent and complementary information. Relative measures,
in particular, have to be interpreted cautiously as they are
heavily dependent on the frequency of disease in the refer-
ent population (incidence rate in the reference popula-
tion). The same absolute difference can result in very
different relative differences depending on the frequency
in the referent group. Point estimates of measures of asso-
ciations are an index of magnitude of effect and are the
best measure for studying causality in epidemiologic
research under biologic hypotheses (Rothman et al.,
2008). It is important to underline that in population-based
studies is not unusual to find small effects with values of
relative risk or odds ratio between 1 and 1.5, whereas in
clinical studies larger effects are found more frequently.
Measures of impact
These are based on absolute and relative measures of
association and provide estimates of the amount of disease
(absolute or relative) that may be caused by a particular
The population attributable risk is the number of new
cases in a defined period that are due to (attributable to) a
particular causative factor. The population attributable
risk (percent or fraction) is the reduction in the incidence
of disease that would be expected in a population if a spe-
cific factor presumed to be causal is removed from the
These approaches are extremely useful for putting in
perspective the value of a specific prevention program as
it quantifies the maximum impact such a program might
have. For example, one can ask questions such as, how
many cases of epilepsy could be prevented in a specific
country if effective measures were put into place to eradi-
cate malaria? Or ‘‘What proportion of epilepsy is due to
endemic neurocystic ercosis (Medina et al., 2005)?’’
Related calculations can yield estimates such as the
attributable risk and attributable risk percent among the
exposed. These are more relevant to making a statement
about the likelihood that a given factor caused a specific
individuals illness.
Formulae for the calculation of these measures and
related statistics can be found in standard textbooks of epi-
demiology (Rothman et al., 2008), neuroepidemiology
(Nelson et al., 2004), and biostati stics (Fleiss et al., 2003).
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
Standards for Epidemiologic Studies and Surveillance of Epilepsy
Page 14
This document proposes definitions, methods, and
resources for investigators of the epidemiology of epi-
lepsy that are intended to promote future population-based
studies of high validity and to improve the comparability
of such studies. Although the emphasis of this article is on
population-based studies, they are useful also in clinical
epidemiologic studies of epilepsy. This guidance is further
intended to be flexible, allowing for differences of pur-
pose among particular studies, as well as variations in the
available methods and information resources on which
they depend.
The authors declare the following associations as potential conflicts
of interest: David J. Thurman None; Ettore Beghi Honoraria/research
grants from Kedrion Pharma Company, Eisai, Sanofi-Aventis, UCB
Pharma, GlaxoSmithKline; Charles E. Begley None; Anne T. Berg
Travel funding/honoraria/consulting fees from Eisai, UCB, Dow Agro
Science; Jeffrey R. Buchhalter Research grants from Ovation Pharma-
ceuticals, Pfizer; Ding Ding None; Dale C. Hesdorffer Honoraria/tra-
vel funds/stock Pfizer, GlaxoSmithKline, General Electric; W. Allen
Hauser Consultant Neuropace; Lewis Kazis Research grants Amgen
Inc., Genzyme, Eli Lilly and Company, Bristol Meyers Squibb, Sanofi-
Aventis, Boerhringer-Ingelheim, and Astra Zeneca; Rosemarie Kobau
None; Barbara Kroner None; David Labiner Consultant/Speaker/
Research grants from Cyberonics, Esai and Ortho McNeil; Kore Liow
Honoraria from UCB Pharma; Giancarlo Logroscino None; Marco T.
Medina None; Charles R. Newton None; Karen Parko None; Ang-
elia Paschal None; Pierre-Marie Preux None; Josemir W. Sander
Honoraria/grants/travel grants from UCB, Janssen-Gilag, Eisai, and
GlaxoSmithKline; Anbesaw Selassie None; William Theodore Hono-
raria/stock Elseveir, General Electric; Torbjçrn Tomson Research
grants/honoraria from Eisai, GlaxoSmithKline, Janssen-Cilag, Novartis,
Pfizer, Sanofi-Aventis, UCB Pharma; Samuel Wiebe None.
We confirm that we have read the Journals position on issues involved in
ethical publication and affirm that this report is consistent with those
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Table A1. Examples of acute symptomatic seizures in association with disruption of the structural
or functional integrity of the brain
Cause (Annegers et al., 1995;
Beghi et al., 2010) Period of occurrence Notes/Exceptions
Cerebrovascular disease (Labovitz et al., 2001;
Camilo & Goldstein, 2004; Hesdorffer et al., 2009)
First 7 days
Traumatic brain injury (Jennett, 1973a,b;
Jennett et al., 1973; Frey, 2003; Hesdorffer
et al., 2009)
First 7 days Includes intracranial surgery. Longer
intervals are acceptable for subdural
hematoma in the absence of known trauma
or at first identification of new hematoma.
Subsequent seizures are unprovoked.
CNS infection (Hesdorffer et al., 2009;
Beghi et al., 2010)
First 7 days Includes seizures occurring after 7 days in
patients with persistent clinical and/or
laboratory signs of infection
Neurocysticercosis Presence of parasites in transitional or
degenerative phase by imaging
Includes seizures occurring in the presence
of mixed forms. Seizures occurring with
viable parasites (acute phase) or calcified
granuloma (calcified phase) are unprovoked
Postmalaria neurologic syndrome
Presence of fever and malaria parasitemia
Clearance of parasitemia, associated with
fever and psychosis
Cerebral tuberculoma During treatment Seizures occurring after successful treatment
are unprovoked
Brain abscess During treatment Seizures occurring after successful treatment
are remote symptomatic
HIV infection Acute infection or with severe metabolic
Seizures occurring in the absence of
opportunistic CNS infections or severe
metabolic disturbances are unprovoked
Arterovenous malformations In the presence of acute hemorrhage All other seizures are unprovoked
Multiple sclerosis First presenting symptom within 7 days
of relapse
Autoimmune diseases Signs or symptoms of activation
Table A2. Comorbid conditions associated with epilepsy
Psychiatric disorders (Hesdorffer et al., 2000; Gaitatzis et al., 2004;
Hesdorffer et al., 2004; Qin & Nordentoft, 2005; Qin et al., 2005;
Hesdorffer et al., 2006, 2007)
Mood disorders, anxiety disorders, alcohol related disorders, somatoform
disorders, attention deficit hyperactivity disorders, schizophrenia
and psychotic disorders, personality disorders, suicidality
Somatic disorders (Gaitatzis et al., 2004) Stroke, cardiovascular disease, diabetes mellitus, migraine, asthma
and other pulmonary conditions, celiac disease and other
gastrointestinal disorders, osteoporosis and osteopenia
Infectious disease (Carpio et al., 1998) Neurocysticercosis
Infestations (Kabore et al., 1996; Pion et al., 2009) Possibly onchocerciasis and toxocara
Cognitive disorders (Elger et al., 2004; Sillanpaa, 2004; Hermann
& Seidenberg, 2007)
Cognitive impairment, learning disability
Disabilities (Gaitatzis et al., 2004) Hearing and vision loss
Accidents (Tomson et al., 2004) Accidents and injuries
Nutritional problems (Crepin et al., 2007) Malnutrition
Epilepsia, 52(Suppl. 7):2–26, 2011
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Appendix: Standard Basic Data
Elements for Epidemiologic
Studies of Epilepsy
The following section presents standard criteria and
coding elements for classifying cases by level of certainty,
a minimum (‘‘core’’) dataset essential for most popula-
tion-based studies of epilepsy, and an expanded
(‘‘optional’’) dataset for studies with resources to obtain
more detailed information.
Case definition criteria
Using data obtained by trained health care provider (inter-
view or medical records)
clear evidence of two or more unprovoked epileptic sei-
zures that have o ccurred over interval(s) exceeding
24 h, OR
confirmed diagnosis of epilepsy by a health care pro-
vider with appro priate speci alized training in the recog-
nition and treatment of epilepsy.
documentation of a diagnosis of epilepsy by a trained
nonspecialist health care provider without specific doc-
umentation of definite criteria above.
data suggest a possibility of epilepsy but crite ria for def-
inite or probable epilepsy are not met. The information
provided is inadequat e to confirm or refute the diagno-
sis of epilepsy.
Using population survey data collected by nonclinician
respondent (subject or proxy) reports that a physician or
trained health care provider has diagnosed epilepsy
information provided suggests a possibility of epileps y
but is inadequate to confirm or refute the diagnosis of
Using data provided by traditional healers or nonclinician
description is available with adequate clinical detail that
is judged by an expert in epilepsy to indicate a high
probability of epilepsy, OR
information is provided by traditional healers whose
abilities to correctly identify cases have been evaluated
and found to be adequate.
description suggests possibility of epilepsy, but criteria
for probable epilepsy are not met.
Using existi ng coded health data (International Classifica-
tion of Diseases)
Note: (1) The specificity and positive predictive values of
ICD-coded medical encounter data have been shown to
vary among studies of epilepsy in different localities
(Holden et al., 2005a; Jette et al., 2010). The following
scheme is suggested as rough guidance where only
coded data are available. An evaluation of the specificity
and predictive values of the following codes and code
combinations in each study locality is advised if possi-
ble, with appropriate modifications of the following
scheme as needed. (2) In most localities, adequate sensi-
tivity may be expected only when complete data can be
linked for both inpatient and outpatient medical encoun-
ters in order to rule out acute symptomatic seizures.
a single medical encounter assigned an ICD-9-CM
diagnostic code 345.xx or ICD-10 code G40.x, OR
two or more medical encounters on separate days each
assigned ICD-9-C M diagnostic codes 780.39 or ICD-10
codes G41.x or R56.8, OR
a single medical encounter assigned ICD-9-CM diag-
nostic codes 780.39 or ICD-10 code R56.8 AND an
AED is prescribed for outpatient use for 3 or more
a single medical encounter is assigned ICD-9-CM code
780.39, or ICD-10 codes R56 .8 or G41.x
Core Data Variables
Case identifier number
Description: A unique identifying code assigned to each
Note: Used for linking data from multiple sources or data
maintained in separate database files.
Birth date
Description: Include month, day, and year of birth, where
Note: An approximation can be used (e.g., year only, or
month and year) if more precise data are unavailable
or not collected to preserve anonymity of subjects.
Missing values for month or day may be estimated.
A precise birth date, in addition to name and sex,
may be necessary to link case records from multiple
sources if these lack common unique identifiers for
each case.
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D. J. Thurma n et al.
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Description: Age at time of case ascertainment by the
study or, in prevalence studies, age on ‘‘prevalence day.’’
Note: Not essential if birth date information is complete
and accurate. If not known precisely, may be estimated,
if necessary, using historical milestones. See also the
separate variable, Age of Onset.
Description: Case classification as female or male.
Locality of residence
Description: May refer to a locality or district, postal code,
city, county, or province or state.
Note: In studies of general populations, may be adapted to
local circumstances, if possible, corresponding to rec-
ognized geopol itical units for which census data are
Race, ethnicity or nationality
Note: Schema are suggested consistent with standard gov-
ernment reporting practices in the jurisdiction under
Current medical treatment status (epilepsy under treat-
ment, not under treatment)
Description: Whether epilepsy is currently under medical
treatment or not.
Epilepsy currently under treatment
Epilepsy not currently under treatment
Unknown treatment status
Note: See also Type of Treatment under optional variables.
Age of onset
Description: The age at which seizures first began.
Note: Alternatively, the date (month and year) can be
used. This is a core variable for studies of incidence,
but is optional for studies of prevalence.
Date of last seizure
Description: Preferably, month, day, and year of most
recent seizure.
Note: Day (and month, if necessary) can be imputed if pre-
cise date not known. Alternative classification may be
most recent occurrence within intervals, for example,
within the past month, or 3 or 6 months, or 1, 2, or
5 years of the date of data collection.
Seizure frequency
Description: For prevalence studies, number of seizures in
the past month, 3 months, and year.
Values: For each interval, record number of reported sei-
Source(s) of data
Description: sources providing data for each case.
Proxy report
Key informants (e.g., teachers, traditional healers, com-
munity leaders)
Prospective clinical history and examination
Retrospective medical record data from clinic, emergency
department (ED), or hospital
Administrative data (ICD-coded hospital, ED, or clinic
Administrative data (pharmacy records)
Vital records (ICD-coded)
Vital records (not coded)
Note: In cases where multiple sources have contributed
data, each should be noted.
Additional Data Variables
The usefulness of the following variables will depend on
the purpose of the study.
Other sociodemographics
Relationship status
Description: current marital (or equivalent) status
Domestic partner (long-term relationship with shared liv-
ing arrangements)
Never married/never a domestic partner
Not applicable (child)
Household composition
Description: Number of persons living in household and
relationships to person with epilepsy. Values: The num-
bers of parents, other adults, and dependent children
should be noted with ages and relationships described.
Educational attainment
Description: Highest level of formal education achieved.
Values: Categories vary greatly among countries. Stan-
dard categories used in reports issued by the relevant
national government should be considered. In some
resource-poor countries, it may be necessary to add
whether or not a person can read and write as a separate
Note: For children with epilepsy, use information pertain-
ing to the parent of highest attai nment with whom they
are living.
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
Standards for Epidemiologic Studies and Surveillance of Epilepsy
Page 22
Employment status
Description: Whether gainfully or otherwise employed.
Values: Categories vary greatly among countries. Stan-
dard categories used in reports issued by the relevant
national government should be considered.
Notes: For children with epilepsy, use information per-
taining to their parent(s) with whom they are living .
Account shoul d be taken of employment in informal
economies, especially in LMIC. If possible, determine
if unemployment is attributed to epilepsy.
Description: Broad vocational categories
Values: Categories vary greatly among countries. Stan-
dard categories used in reports issued by the relevant
national governmentor categories used by the Inter-
national Labor Organization (International Labor Orga-
nization, 1990) or the United Nations (Statistical
Commission on International Econom ic and Social
Classifications, 2008)shoul d be considered.
Note: For children with epilepsy, use information pertain-
ing to their parents with whom they are living. The
occupations of homemaker and parental childca re pro-
vider shoul d be included, although these are omitted
from some standard classifications.
Personal income
Description: Annual income reported by person with epi-
Values: Relevant inco me strata vary among countries.
Standard categories used in reports issued by the rele-
vant national government may be considered.
Note: Source of income (e.g., earned income or disability
allowance) may be considered. Not applicable for chil-
Household income
Description: Annu al income reported for household in
which person with epilepsy resides.
Values: Relevant inco me strata vary among countries.
Standard categories used in reports issued by the rele-
vant national government may be considered. In the
United States, relevant categories might be defined in
terms of the percent of the Federal Poverty Level.
Note: Applicable for children
Health insurance
Description: Type of health insurance, if any, covering
person with epilepsy.
Values: Relevant categories vary among countries and at a
minimum, private insurance should be separated from
public insurance coverage. In the United States, rele-
vant categories might include:
Private insurance
Medicaid (SCHIP for children)
Other government-sponsored insurance
Seizure type and syndrome
Seizure type
Description: Ideally, the determination of seizure type is
based on detailed clinical descriptions supplemented
with information from EEG, neuroimaging studies, and
examination findings. In populations where such infor-
mation is routinely available, the ILAE classification
(Engel, 2006b; Berg et al., 2010) may be considered for
epidemiologic studies. In the absence of such detailed
information simplifications to that classification may
be useful. More simplifie d levels of classification rely
to a greater extent on the predominant ictal semiology.
However, they still allow information from studies with
more classification detail to be collapsed onto the same
meaningful categories, enabling comparisons across
disparate settings. Where cases experience more than
one seizure type; each type should be described.
Based on clinical datasee Appendix, Table A1 for sim-
plified classification or see ILAE classification (Engel,
Based on ICD-coded datasee table below. Note that
accuracy of ICD-9-CM and ICD-10 coding to the level
of the fourth and fifth digits has not been extensively
Table A3. Simplified Seizure Classification
by mode of onset, based on ICD-coded data
ICD-9 ICD-10
nonconvulsive not
G40.4, G40.6
Nonconvulsive 345.0, 345.2; G40.7, G41.1
Convulsive 345.1, 345.3
Simple, dyscognitive
(complex) not
Simple 345.5 G40.1
Dyscognitive (complex) 345.4 G40.2, G41.2
Unclassified 345.6–345.9,
G40.5, G40.8,
G40.9, G41.0, G41.8,
G41.9, R56.
Epilepsia, 52(Suppl. 7):2–26, 2011
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D. J. Thurma n et al.
Page 23
Epilepsy syndrome
The ILAE provides a list of epilepsy syndromes that
is periodically revised (Commission on Classification
and Terminology of the International League Against
Epilepsy 1989; Engel, 2006a; Berg et al., 2010). The
classification requires detailed clinical and electroenceph-
alographic information that may not be available for
epidemiologic studies.
Note: Where adequate data are available, the full ILAE
classification may be used.
Etiologies and comorbid conditions
Description: Direct cause of epilepsy.
Values: See Appendix, Table A3.
Stability of underlying condition
Note: This is primarily applicable to causes classified as
‘‘structural/metabolic’’ or ‘‘unknown.’’
Neurological status
Description: broad asse ssment of overall neurological
function and subcategories of cognitive, motor, and
sensory function. The purpose is to identify persons
who, in addition to epilepsy, also have substantial
motor, cognitive, or sensory impairments sufficient to
interfere with normal education, employment, or inde-
pendent living.
Values (indicate each as normal or impaired):
General Neurologic Status
If impaired, specify:
Cognitive function
Motor function
Sensory function (vision and hearing)
Note: If cognitive or motor impairments are congenital or
acquired early in life, that is, cerebral palsy (Christine
et al., 2007) or intellectual disability (mental retarda-
tion) (Shevell, 2008), this should be specified.
Description: Defined as either (1) any medical or psychiat-
ric condition that may occur or coexist with epilepsy or
(2) those conditions that occur or coexist with epilepsy
at rates that are greater than expected in general popula-
tions. For the former, classifications of general morbid-
ity indices are advised (Linn et al., 1968; Charlson
et al., 1987; Deyo et al., 1992; Miller et al., 1992; Dodds
et al., 1993; Elixhauser et al., 1998; Selim et al., 2004).
For the latter, describe specific conditions of interest to
the study. These may be grouped under broad categories
of psychiatric disorders, neurologic and special sen-
sory disorders, and general medical conditions.
Current medical treatment status (epilepsy under treat-
ment, not under treatment)
Description: Whether epilepsy is currently under medical
treatment or not.
Epilepsy currently under treatment
Epilepsy not currently under treatment
Unknown treatment status
Note: See also Type of Treatment under optional vari-
Medical therapy
Description: Type of treatment received for epilepsy.
AED(s) (record and specify all AEDs used)
Diet therapy (e.g., ketogenic diet)
Stimulator/device (vagus nerve or brain)
Epilepsy surgery (e.g., past cortical resection, hemispher-
ectomy, callosotomy, multiple subpial transection)
Traditional medicine or treatment (specify)
Other (specify)
No current or past medical therapy for epilepsy
Note: Record all that apply. Record whether each type of
therapy is current (ongoing) or past (discontinued or
completed). If all AEDs discontinued, record date last
AED was stopped.
Most recent AED use
Description: Interval from time of assessment to time the
last dose of AED was taken.
Values: Record the number of hours since last AED dose.
Note: Use longer unit(s) of time, if applicable.
Other care
Description: Other supportive care received for epilepsy
or its consequences.
Occupational or physical therapy
Vocational rehabilitation
Individual Educational Plan (children)
Other special educational support
Other health service support (e.g., childrens health plan)
No current or past services for epileps y recorded
Note: Record all that apply. Record whether each type of
service is current (ongoing) or past (discontinued or
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
Standards for Epidemiologic Studies and Surveillance of Epilepsy
Page 24
Emergency treatment
Description: Number of occas ions of emergency/acute
medical treatment (e.g., intravenous, rectal, or buccal
administration of benzodiazepine) for seizures in the
past month, 3 months, and year.
Values: For each interval, record number of times emer-
gency treatment was received, specify type of emer-
gency treatment, and whether administered in hospital
or not.
Other variables
The National Institutes of Health has developed common
data elements for several diseases. These may provide
supplemental information on data collection for research-
ers worldwide and can be viewed at http://www.common Included are
individual variables as well as recommended standard-
ized instruments for the measurement of different fac-
tors such as quality of life, cognition, and comorbidity.
Epilepsia, 52(Suppl. 7):2–26, 2011
doi: 10.1111/j.1528-1167.2011.03121.x
D. J. Thurma n et al.
Page 25
  • Source
    • "This is opposed to what normally happens, when an excitatory neuron fires it becomes resilient to firing again for a short period of time (Else and Hammer, 2013 ). There are approximately 65 million people worldwide living with epilepsy (Thurman et al., 2011 ). It varies from region to region, for instance in the United States the annual incidence of epilepsy is 48 per 100,000 inhabitants, whereas the prevalence approximates 710 per 100,000 (Hirtz et al., 2007). "
    [Show abstract] [Hide abstract] ABSTRACT: Background: The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. Methods: In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. Results: Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. Comparison with existing methods: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. Conclusions: The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.
    Full-text · Article · Apr 2016 · Journal of Neuroscience Methods
  • Source
    • "Epilepsy is a disabling neurological disorder that affects up to 65 million people worldwide and is characterized by spontaneous and recurrent occurrence of seizures (Thurman et al., 2011). Although epileptic seizures are generally thought to be caused by an increased state of brain excitability, it is much less clear what is causing this hyperexcitability (Devinsky et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Recent evidence points at an important role of endogenous cell-damage induced pro-inflammatory molecules in the generation of epileptic seizures. Uric acid, under the form of monosodium urate crystals, has shown to have pro-inflammatory properties in the body, but less is known about its role in seizure generation. This study aimed to unravel the contribution of uric acid to seizure generation in a mouse model for acute limbic seizures. We measured extracellular levels of uric acid in the brain and modulated them using complementary pharmacological and genetic tools. Local extracellular uric acid levels increased three to four times during acute limbic seizures and peaked between 50 and 100min after kainic acid infusion. Manipulating uric acid levels through administration of allopurinol or knock-out of urate oxidase significantly altered the number of generalized seizures, decreasing and increasing them by a twofold respectively. Taken together, our results consistently show that uric acid is released during limbic seizures and suggest that uric acid facilitates seizure generalization.
    Full-text · Article · Jan 2016 · Experimental Neurology
    • "As with most chronic conditions, the prognosis of epilepsy depends on the characteristics of the population at risk, the definition of seizures and epilepsy used, the duration of follow-up, and the presence of selected prognostic predictors, including treatment. Epilepsy must be differentiated from acute symptomatic seizures and single unprovoked seizures (Thurman et al., 2011). Acute symptomatic seizures are those occurring in close temporal relationship to an acute CNS insult, which may be metabolic, toxic, structural, infectious, or due to inflammation (Beghi et al., 2010). "
    [Show abstract] [Hide abstract] ABSTRACT: Epilepsy is a brain condition characterized by the recurrence of unprovoked seizures. Generally, prognosis refers to the probability of attaining seizure freedom on treatment and little is known about the natural history of the untreated condition. Here, we summarize aspects of the prognosis and prognostic predictors of treated and untreated epilepsy and of its different syndromes. Usually, epilepsy is a fairly benign condition. Most epilepsies have a good prognosis for full seizure control and eventual discontinuation of AEDs, but epilepsy syndromes have differing outcomes and responses to treatment. Prognostic factors include aetiology, EEG abnormalities, type of seizures and the number of seizures experienced before treatment onset, and poor early effects of drugs. Early response to treatment is an important positive predictor of long-term prognosis, while the history of a high number of seizures at the time of diagnosis, intellectual disability, and symptomatic aetiology are negative predictors. Different prognostic patterns can be identified, suggesting that the epileptogenic process is not static. Epilepsy carries a greater than expected risk of premature death. Aetiology is the single most important risk factor for premature death.
    No preview · Article · Jul 2015 · Epileptic disorders: international epilepsy journal with videotape
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