Psychology, 2010, 1: 9-16
doi:10.4236/psych.2010.11002 Published Online April 2010 (http://www.SciRP.org/journal/psych)
9
Sensitivity and Specificity of the CDC Empirical
Chronic Fatigue Syndrome Case Definition
Leonard A. Jason, Meredyth Evans, Abigail Brown, Molly Brown, Nicole Porter, Jessica Hunnell,
Valerie Anderson, Athena Lerch
DePaul University, Chicago, American.
Received February 10th, 2010; revised March 7th, 2010; accepted March 8th, 2010.
ABSTRACT
In an effort to bring mo re standardization to the chron ic fatigue syndrome (CFS) Fukuda et al. case definition [1], the
Centers for Disease Control and Prevention (CDC) has developed an empirical case definition [2] that specifies crite-
ria and instruments to diagnose CFS. The pr esent study investiga ted the sens itivity and specificity o f this CFS emp irical
case definition with diagnosed individuals with CFS from a community based study that were compared to non-CFS
cases. All participants completed questionnaires measuring disability (Medical Outcome Survey Short-Form-36) [3],
fatigue (the Multidimensional Fatigue Inventory) [4], and symptoms (CDC Symptom Inventory) [5]. Findings of the
present study indica ted sensitivity and specificity problems with the CDC empirical CFS case definition.
Keywords: Chronic Fatigue Syndrome, Empirical Case Definition, Centers for Disease Control and Prevention,
Fukuda Criteria
1. Sensitivity and Specificity of the CDC
Empirical Case Definiti on
The Centers for Disease Control and Prevention (CDC)
has developed an empirical case definition for chronic
fatigue syndrome (CFS) that involves assessment of
symptoms, disability, and fatigue [2]. The CDC empiri-
cal CFS case definition assesses three specific areas to
determine whether a person meets criteria for this illness
including: 1) disability, using the Medical Outcomes
Survey Short Form-36 (SF-36) [3], 2) fatigue, using the
Multidimensional Fatigue Inventory (MFI) [4], and 3)
symptoms, using the CDC Symptom Inventory (SI) [5].
The authors of this empirical case definition feel that the
specification of instruments and cut-off points will result
in a more reliable and valid approach for the assessment
of CFS.
The disability criterion for the Reeves et al. empirical
CFS case definition [2] would be met by scoring below
the 25th percentile on any one of the following four
SF-36 sub-scales [3]: Physical Functioning (less than or
equal to 70), Role Physical (less than or equal to 50),
Social Functioning (less than or equal to 75), or Role
Emotional (less than or equal to 66.7). Because a person
could meet the disability criterion for the empirical CFS
case definition by only showing impairment in one or
more of these four areas, a person could meet the disabil-
ity CFS criterion by only having an impairment in role
emotional areas (e.g., problems with work or other daily
activities as a result of emotional problems). Ware et al.
[3] found that the mean for Role Emotional for a clinical
depression group was 38.9, indicating that almost all
those with clinical depression would meet the CFS dis-
ability criterion, as they would be within the lower 25th
percentile on this sub-scale.
To meet the fatigue criterion, the Reeves et al. empiri-
cal case definition [2] requires a score on the MFI [4] of
greater than or equal to 13 on the General Fatigue sub-
scale, or greater than or equal to 10 on the Reduced Ac-
tivity sub-scale. In one study of three groups with CFS
[6], the mean MFI General Fatigue scores ranged from
18.3 to 18.8 and these scores are clearly higher than the
Reeves et al. cutoff of 13. In addition, Reduced Activity
items refer to issues that a person with depression might
easily endorse. If a person indicated that the following
two items were entirely true: “I get little done,” and “I
think I do very little in a day”; they would meet criterion
for fatigue on this sub-scale.
The SI [5] assesses information about the presence,
frequency, and intensity of fatigue related symptoms
during the past one month. The frequency and severity
scores were multiplied for each of the eight critical Fu-
kuda et al. [1] symptoms and were then summed. To
meet the Reeves et al. [2] symptom criterion, a person

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 10
needed to have four or more symptoms and a total score
greater or equal to 25 on the SI. This overall level of
symptoms seems relatively low for patients with classic
CFS symptoms (the criterion would be met if an indi-
vidual rated only 2 core symptoms as occurring all the
time, and if one was of moderate and the other of severe
severity). In addition, the 8 case definition symptoms for
the empirical case definition were based on a time period
comprising the last month compared to what is specified
in the Fukuda et al. criteria, which states that: “There
needs to be the concurrent occurrence of 4 or more of the
following symptoms, and all must be persistent or recur-
rent during 6 or more months of the illness and not pre-
date the fatigue.”
Jason, Najer, Porter, and Reh [7] recently investigated
this CFS empirical case definition with 27 participants
with a diagnosis of CFS and 37 participants with a diag-
nosis of a Major Depressive Disorder (MDD). All par-
ticipants completed questionnaires measuring disability
(SF-36), fatigue (MFI), and symptoms (SI). Jason et al.
found that 38% of those with a diagnosis of MDD were
misclassified as having CFS using the new CDC empiri-
cal case definition. Jason, Evans, et al. [8] later used this
same sample to examine issues of sensitivity and speci-
ficity for the three instruments along with their cut-off
points. Sensitivity is the probability that the test correctly
classifies a person with CFS as positive, whereas
specificity is the probability that a test correctly classifies
a person without CFS as negative. When Jason, Evans, et
al. used a Receiver Operating Characteristic (ROC)
curve analysis with the Reeves et al. criteria [2], they
found the disability, fatigue and symptom criteria had
serious specificity and/or sensitivity problems. They
concluded that the Reeves et al. criteria would not be
considered a good diagnostic method for selecting CFS
cases among a sample of CFS and MDD cases.
Reeves, Gurbaxani, Lin, and Unger [9] critiqued the
study by Jason et al. [7] by stating that the study should
have relied on better methods to diagnose the sample,
including a medical and psychiatric examination. An-
other criticism brought up by Reeves et al. was the focus
on MDD, particularly as some persons with CFS also
suffer from MDD. Some individuals with CFS do have
MDD, but the key issue is that MDD can be confused
with CFS, as it has some overlapping symptoms with
CFS. For example, it is possible that some patients with
MDD also have chronic fatigue and four CFS Fukuda
et al. [1] symptoms that can occur with depression (e.g.,
unrefreshing sleep, joint pain, muscle pain, impairment
in concentration). Yet, CFS and MDD are different dis-
orders, and they can be differentiated by use of appropri-
ate assessment instruments [10].
Great care needs to be exercised when determining
which scales, with which cut off points, should indicate
that CFS criteria has been reached for CFS samples. For
example, Jason, Brown, et al. [11] examined published
studies using the SF-36 [3] which contrasted CFS with
controls. The largest differences emerged for the Role
Physical, Social Functioning, and Vitality SF-36 sub-
scales. Rather than arbitrarily selecting the lower 25% for
four SF-36 sub-scales, as was recommended by the au-
thors of the empirical CDC CFS case definition [2], Ja-
son, Brown, et al. used Receiver Operating Characteris-
tics (ROC) to determine sub-scales that best discriminate
CFS from Controls in two well defined samples, one
involving a community data base collected in the mid
1990s, and the other a tertiary data base collected in the
mid 2000s. Vitality, Social Functioning, and Role Physi-
cal had the highest AUCs, with good sensitivity and
specificity.
Because the Jason, Brown, et al. study [11] only had
data on the SF-36, these investigators were not able to
examine the Reeves et al. [2] recommendations on fa-
tigue or symptom criteria. In addition, the Jason et al. [7]
sample, which had all three Reeves et al. measures, had
been criticized as not having formal medical and psychi-
atric examinations to select cases. The present study in-
cludes the disability, fatigue, and symptom measures as
recommended by Reeves et al. in a carefully defined
sample. In this study, we employed an ROC to determine
the sensitivity and specificity of the Reeves et al. criteria
in a well characterized community-based CFS sample.
This study included formal medical and psychiatric tests
to determine CFS status.
2. Method
The present project was carried out in two stages. In
Stage 1, we attempted to re-contact the 213 adults who
were medically and psychiatrically evaluated from a
community-based sample from 1995-1997. These adults
were previously evaluated in our original Wave 1 CFS
epidemiology project [12]. Stage 2 of the study encom-
passed a structured psychiatric assessment, a complete
physical examination and a structured medical history.
The original Wave 1 sample collected from 1995-1997
is a stratified random sample of several neighborhoods in
Chicago specifically selected to contain individuals from
different ethnic and socioeconomic profiles. As a whole,
Chicago, Illinois is an ethnically and socioeconomically
diverse city. We sampled in eight Chicago community
locations, including low socioeconomic areas such as
West Garfield Park, middle-socioeconomic areas such as
Bridgeport and Armour Park, gentrifying areas such as
the near West Side, and high socioeconomic areas such
as the Loop and the near North Side. Racial data indicate
that the sample consists of 20.0% African-Americans,
52.6% Caucasians, 18.7% Latinos, 0.5% Native Ameri-
cans, 5.5% Asian Americans, 1.4% multiracial individu-
als, and 1.3% individuals of other races [12]. The tele-
phone numbers comprising the stratified random sample

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 11
were obtained from Survey Sampling, Incorporated. This
company generated random telephone numbers using
valid Chicago exchanges, resulting in a sample of both
listed and unlisted numbers (as well as business and
non-working numbers). In the first stage of data collec-
tion in the original study, procedures developed by Kish
[13] were used to select one adult from each household
for subsequent screening for CFS-like illness. Birth dates
for each adult were gathered and the person with the
most recent birthday was selected to be interviewed us-
ing the Stage 1 CFS Screening Questionnaire. The final
sample of respondents consisted of 18,675 households.
2.1 Stage 1
The CFS Screening Questionnaire consists of two parts
and was administered to all participants that could be
located for this follow-up study. It assessed partici-
pants’ sociodemographic characteristics and fatigue
characteristics to determine whether any changes have
occurred since the first wave of data collection in the
original study. Basic demographic data included age,
ethnicity, socioeconomic status, work status, marital
status, parental status (including number of children)
and gender. Consistent with the procedures followed in
the original CFS epidemiology study [12] the CFS
Screening Questionnaire contains questions measuring
more specific aspects of fatigue and health status. In
addition, questions assessed the level of impairment that
fatigue and illness cause to daily activities, as well as
the frequency and duration of the fatigue. Respondents
were also asked if they have ever been diagnosed with
any other medical or psychiatric conditions associated
with chronic fatigue and what current treatments they
were receiving. A version of the screening scale used in
the present study was evaluated by Jason et al. [14].
They recruited four groups of subjects (i.e., those diag-
nosed with CFS, lupus, and multiple sclerosis, and a
healthy control group). All subjects were interviewed
with a screening instrument twice over a two-week pe-
riod of time. The screening scale exhibited high dis-
criminant validity and excellent test-retest and in-
ter-rater reliability. Hawk et al. [10] revised this CFS
Screening Questionnaire, and administered the ques-
tionnaire to three groups (those with CFS, MDD, and
healthy controls). The revised instrument, which was
used in the present study, evidences good test-retest
reliability and has good sensitivity and specificity.
2.2 Stage 2
In Stage 2, the Structured Clinical Interview for the
DSM-IV (SCID) [15] was administered to assess current
psychiatric diagnoses as defined on Axis I of the Diag-
nostic and Statistical Manual of Mental Disorders –
Fourth Edition (DSM-IV) [16]. The SCID is a valid and
reliable semi-structured interview guide that approxi-
mates a traditional psychiatric interview [17]. It has been
successfully used to assess psychiatric disorders in sam-
ples of people with CFS [18].
Following the structured psychiatric interview, par-
ticipants were provided a medical history interview and
complete medical examination. Prior to the physical ex-
amination, the interviewer who accompanied participants
and provided transportation to the medical exam admin-
istered the Medical Questionnaire at the physician’s of-
fice to assess current and past medical history. The
Medical Questionnaire is a modified version of The
Chronic Fatigue Questionnaire, a structured instrument
developed by Komaroff and Buchwald [19] that was
used in a study by Komaroff et al. [20]. This comprehen-
sive instrument assesses symptoms related to CFS and
chronic fatigue, as well as other medical and psychiatric
symptoms, in order to help rule out exclusionary condi-
tions such as HIV/AIDS, active malignancies, iatrogenic
conditions resulting from the side effects of medication,
unresolved cases of hepatitis, and active substance use.
In addition, the Medical Questionnaire measures fatigue
severity, fatigue-related social role impairment, psycho-
social stressors, job satisfaction, toxic exposures prior to
CFS onset, chemical sensitivities, presence of CFS or
chronic fatigue in other network members, and family
medical history. Because sleep disturbances are often
reported by individuals with CFS and chronic fatigue, the
Sleep Disturbance Questionnaire, which has been vali-
dated experimentally in a sleep laboratory [21], has been
incorporated into the medical questionnaire to help iden-
tify participants with sleep disorders.
Participants also filled out the Medical Outcome Sur-
vey Short-Form-36 (SF-36) [3]. This 36-item instrument
is composed of multi-item scales that assess functional
impairment in eight areas: limits in physical activities
(physical functioning), limits in one’s usual role activi-
ties due to physical health (role physical), limits in one’s
usual role activities due to emotional health (role emo-
tional), bodily pain, general health perceptions (general
health), energy and fatigue (vitality), social functioning,
and general mental health. Scores in each area reflect
ability to function and higher values indicate better func-
tioning. Reliability and validity studies have demon-
strated high reliability and validity in a wide variety of
patient populations for this instrument [22]. According to
Reeves et al. [2] significant reductions in occupational,
educational, social, or recreational activities were defined
as scores lower than the 25th percentile on Physical Func-
tioning (less than or equal to 70), or Role Physical (less
than or equal to 50), or Social Functioning (less than or
equal to 75), or Role Emotional (less than or equal to
66.7). A person would meet the disability criterion for
the empirical CFS case definition by showing impair-
ment in one or more of these four areas.
Participants also completed the CDC Symptom Inven-

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 12
tory (SI) [5]. The SI assesses information about the
presence, frequency, and intensity of 19 fatigue related
symptoms during the past one month. For each of the
eight Fukuda et al. [1] symptoms, participants were
asked to report the frequency (1 = a little of the time, 2 =
some of the time, 3 = most of the time, 4 = all of the time)
and severity (the ratings were transformed to the follow-
ing scale: 0 = symptom not reported, 1 = mild, 2.5 =
moderate, 4 = severe)1. The frequency and severity
scores were multiplied for each of the eight critical Fu-
kuda et al. symptoms and were then summed. Individuals
having four or more symptoms and scoring greater or
equal to 25 would meet symptom criterion on this in-
strument according to the CDC empirical case definition.
Additionally, the participants completed the Multidi-
mensional Fatigue Inventory (MFI) [4]. This instrument
is a 20-item self-report instrument consisting of five
scales: general fatigue, physical fatigue, reduced activity,
reduced motivation, and mental fatigue. Each scale con-
tains four items rated from 1 to 5 with the scale score of
1= completely true and the scale score of 5 = no, not true.
Reeves et al. [2] employed the MFI to measure severe
fatigue, and to do this, they used only two of the five
subscales; General Fatigue and Reduced Activity. Using
the CDC empirical case definition standards, severe fa-
tigue was defined as greater than or equal to 13 on Gen-
eral Fatigue or greater than or equal to ten on Reduced
Activity.
Following the medical history interview, the physician
conducted a detailed medical examination. This exami-
nation was carried out in order to rule out exclusionary
medical conditions and detect evidence of diffuse ade-
nopathy, hepatosplenomegaly, synovitis, neuropathy,
myopathy, cardiac or pulmonary dysfunction, or any
other medical disorder. An 18-tender-point examination
was used to test for Fibromyalgia [23]. Laboratory tests
administered to all participants included a chemistry
screen (glucose, calcium, electrolytes, uric acid, liver
function tests, and renal function tests), complete blood
count with differential and platelet count, T4 and TSH,
erythrocyte sedimentation rate, arthritic profile (which
includes rheumatoid factor and antinuclear antibody),
hepatitis B surface antigen, CPK, HIV screen, and uri-
nalysis. An intra-dermal, intermediate-strength PPD skin
test was applied, and a posterior-anterior chest x-ray was
completed, if it was not already obtained by the partici-
pant within eight months of entering the study. At the
time of evaluation, the examining physician was blinded
to participants’ status with respect to initial classification
based upon the Stage 1 screen. Participants were reim-
bursed $100.00 for the time and effort involved in par-
ticipation. Participants also signed the Human Subjects
Consent Form (See Jason, Porter, Hunnell, Rademaker,
& Richman [24] for more details).
At the end of Stage 2, a team of physicians was re-
sponsible for making final diagnoses. Two physicians
independently rated each file according to the current
U.S. definition of CFS. Files that did not meet CFS crite-
ria were rated as either idiopathic chronic fatigue (ICF),
exclusionary for CFS due to medically/psychiatrically
explained chronic fatigue [1], or control (participants
with no exclusionary illness and less than 6 months of
fatigue). Those with ICF had at least six months duration
of fatigue, but with insufficient symptoms or fatigue to
meet the case definition of CFS. The exclusionary group
had chronic fatigue for at least six months duration, but
with active medical conditions that explain chronic fa-
tigue (e.g., untreated hypothyroidism), previously diag-
nosed medical disorders whose resolution has not been
documented beyond reasonable clinical doubt, and whose
continued activity may explain the chronic fatiguing ill-
ness (e.g., unresolved cases of hepatitis C). The exclu-
sionary group also included those with chronic fatigue
for at least six months duration, but with psychiatric ex-
planations of the fatigue (e.g., delusional disorders,
schizophrenia, etc). Controls had no exclusionary ill-
nesses and less than 6 months of fatigue. Reviewing phy-
sicians had access to all information gathered on each
participant during each of the phases of the study. The
review panel was also provided with all results from the
physical exam. If a disagreement occurred during the
physician review process regarding whether a participant
should receive a diagnosis of CFS, ICF, exclusionary due
to medically/psychiatrically explained chronic fatigue, or
control, the participant’s file was rated by a third physi-
cian reviewer, and the diagnosis was determined by ma-
jority rule. We used refinements of the Fukuda et al. cri-
teria as recommended by an International Research group
and the CDC [25].
2.3 Sample Characteristics
In Wave 1, 213 adults were medically and psychiatrically
evaluated from the community-based sample. For the
follow-up study, data was available on 24 individuals
diagnosed with CFS and 84 who did not have CFS. Wave
1 differences were examined between those we were able
versus those we were not able to re-evaluate at Wave 2,
and we did not find any significant sociodemographic
differences for age, gender, race, marital status, number
of children, or education (See Jason, Porter, et al., [24]
for more details).
2.4 Statistical Analysis
The statistical software package used for data analysis
was PASW (formerly SPSS) for Windows, version 17.0.
A Receiver Operating Characteristic (ROC) curve analy-
sis [26] was used to evaluate the ability of the scales to
1The scale we used had five choices, and we needed to convert the
ratings to a four point scale in order to conform to Wagner et al.’s
(2005) severity scaling system.

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 13
discriminate between patients with CFS in the commu-
nity-based sample and those without this illness. The
ROC curve graphically represents the probability of true
positive results in diagnosis as a function of the
probability of false positive results of this test. The area
under the curve (AUC) is an indicator of the discrimina-
tory ability of the scale: a straight line (area = 0.5) means
that the scale is doing no better than chance in classifying
CFS and non-CFS, while a perfect scale would have an
ROC curve with an area of 1. The area under the ROC
curve is a summary measure that essentially averages
diagnostic accuracy across the spectrum of test values.
The informative area under the ROC curve ranges from
0.5 to 1.0, and not from 0.0 to 1.0 as would the area un-
der a probability distribution curve. An AUC of .99
means that 99% of the time a randomly selected individ-
ual from the CFS group will more adequately fulfill the
fatigue criteria than a randomly selected individual from
the control group. A test needs an AUC threshold of be-
tween 90-100% to have diagnostic meaning, and 95% or
above to be considered a good diagnostic tool [27,28].
3. Results
3.1 ROC Analyses
Table 1 presents the ROC analyses for the CFS versus
the non-CFS group. The MFI scales had AUCs that were
low. When using the cutoff scores proposed by Reeves
et al. [2], using either the General Fatigue or Reduced
Activity criteria, 95% of those with CFS were identified,
indicating good sensitivity, but the specificity was
only .27, indicating that few of those without the illness
would have been correctly identified. The AUC for the
SI instrument was also low, and the sensitivity data (.59)
suggests that this symptom scale has significant problems
in identifying true cases of CFS. Finally, AUC findings
for the SF-36 indicate low AUCs, and using Reeves
et al.’s cutoff scores, that the sensitivity is acceptable
at .96; however, specificity is inadequate at .17. When
using all three criteria for fatigue, symptoms and disability,
the sensitivity was at an unacceptably low level of .65. The
sensitivity and specificity outcomes for the Reeves et al.
criteria suggest that these recommended scales and cutoff
points would not be considered a good diagnostic tool for
selecting CFS cases from the general population.
4. Discussion
The present study investigated the sensitivity and speci-
ficity of the empirical CFS case definition [2] with diag-
nosed individuals with CFS from a community based
study that were compared with non-CFS cases. Findings
of the present study indicated sensitivity and specificity
problems with the CDC empirical CFS case definition.
When comparing the overall Reeves et al. criteria, only
Table 1. AUC values, standard errors and confidence intervals for CFS vs. other*
Scale AUC Std. Error 95% C.I. Cut-offs Sensitivity Specificity
LB UB
MFI
Gen. Fatigue 0.69 0.07 0.56 0.82 ≥ 13 0.74 0.39
Red. Activity 0.64 0.07 0.51 0.78 ≥ 10 0.74 0.50
Meetsa 0.61 0.07 0.47 0.74 0.95 0.27
SI
Totalb 0.69 0.07 0.55 0.84 ≥ 25 0.59 0.73
SF-36
Phys. Func. 0.60 0.06 0.48 0.72 ≤ 70 0.68 0.51
Role Phy. 0.66 0.06 0.54 0.77 ≤ 50 0.82 0.51
Soc. Func. 0.62 0.07 0.48 0.76 ≤ 75 0.74 0.35
Role Emo. 0.57 0.07 0.43 0.70 ≤ 66.7 0.73 0.44
Meetsc 0.56 0.07 0.44 0.69 0.96 0.17
Meets Criteriad 0.70 0.08 0.56 0.85 0.65 0.76
aMeets Reeves et al. (2005) fatigue criteria.
bMeets Reeves et al. (2005) core symptoms criteria.
cMeets Reeves et al. (2005) substantial reductions criteria.
dMeets Reeves et al. (2005) CFS criteria.
*Some of the participants did not complete all three questionnaires, and were thus excluded from the overall sensitivity and specificity figures.

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 14
about 65% of true CFS cases were identified. In other
words, these criteria are not able to identify an acceptable
high percentage of individuals who have this illness.
If samples of CFS are not identified with sensitivity
and specificity, it will be difficult to compare samples
from different studies, and the search for biological
markers will be compromised. Using the Reeves et al.
criteria [2], the estimated rates of CFS have increased to
2.54% [29], rates that are about ten times higher than
prior CDC estimates [30] and prevalence estimates of
other investigators [31]. It is at least possible that the
increases in the United States are due to a broadening of
the case definition and possible inclusion of cases with
primary psychiatric conditions. Chronic fatigue occurs in
about 4-5% of the population [32]. If about 5% of the
population has 6 or more months of fatigue, and about
half of this is due to clear medical or psychiatric reasons
[31], then the critical question is how many of the re-
maining 2.5% have CFS. The empirical CFS case defini-
tion estimates that 2.54% do have this illness, so that
research group would suggest that almost all of the re-
maining 2.5% would fall within the CFS category.
However, Jason et al. [7] believe that within this 2.54%
are mood disorders, which are one of the most prevalent
psychiatric disorders (one-month prevalence rate of ma-
jor depressive episode is 2.2%) [33]. As an example, one
mood disorder is MDD, which can be confused with CFS,
as it has some overlapping symptoms with CFS. It is
possible that some patients with MDD also have chronic
fatigue and four CFS Fukuda et al. [1] symptoms that can
occur with depression (e.g., unrefreshing sleep, joint pain,
muscle pain, impairment in concentration). Fatigue and
these four minor symptoms are also defining criteria for
CFS, so it is possible that some patients with a primary
affective disorder could be misdiagnosed as having CFS.
Yet, these are distinct illnesses, as several CFS symp-
toms are not commonly found in depression, including
prolonged fatigue after physical exertion, night sweats,
sore throat, and swollen lymph nodes. Illness onset with
CFS often occurs over a few hours or days, whereas pri-
mary depression generally shows a more gradual onset.
Biological findings also differentiate the two conditions
[34]. Including the latter type of patients in the current
CFS case definition could confound the interpretation of
epidemiologic and treatment studies, and complicate ef-
forts to identify biological markers for this illness.
It is important for screening tests to have high sensi-
tivity and specificity, particularly for disorders with low
prevalence rates such as CFS (about 4.2 in a thousand)
[31]. As an example, in a city of 1,000,000, with a true
CFS rate of 4.2 per thousand, there would be 4,200 CFS
cases. According to Bayes’ theorem [35] if a diagnostic
test had a 95% rate of sensitivity, the screening test
would correctly identify 3,990 of these cases. However,
if the test had 95% specificity, there would be 49,790
individuals who did not have CFS but were identified as
having it using the test. Clearly, being able to identify
true negatives with precision is of high importance in the
diagnostic process.
We provide two case studies that illustrate several of
the problems with the Reeves et al. [2] criteria. For ex-
ample, one person who we diagnosed with CFS did not
meet the Reeves et al. empirical case definition due to
not meeting the frequency/severity requirement for the
Symptom Inventory (SI). Yet, this person indicated that
she had experienced a 95% decrease in daily activities
over the past 6 months and an 80% decrease in daily en-
ergy level over the last 6 months. The person also re-
ported having experienced 6 months of fatigue and more
than 4 core symptoms. On a different scale from the
medical questionnaire, using a 100 point scale, with
higher scores indicating more problems, the person had a
score of 80 on impaired memory and 85 on un-refreshing
sleep. Our physician panel clearly felt that this person
met all CFS Fukuda et al. [1] criteria, but the person was
not included as a CFS case using the Reeves et al. crite-
ria. In contrast, another person who we classified as
ICF met the Reeves et al. empirical case definition. This
person only had a 30% reduction in daily activity in the
last 6 months and a 30% reduction of daily energy levels
in the last 6 months. Our physician panel did not diag-
nose this participant has having CFS, yet the person was
counted as a CFS case using the Reeves et al . criteria.
There are several limitations in this study. First, the
community-based study of participants was relatively
small. Clearly, these results need to be replicated by
other investigators with larger samples. However, when
the Reeves et al. [2] disability criteria were evaluated on
a tertiary care setting [11], the findings also pointed to
sensitivity and specificity problems. Another study using
psychiatric controls also found the empirical case defini-
tion to be problematic due to specificity issues [7].
In summary, the scientific enterprise depends on reli-
able and valid ways of classifying patients into diagnos-
tic categories, and this critical research activity can en-
able investigators to better understand etiology, patho-
physiology, and treatment approaches for CFS and other
disorders [36]. When diagnostic categories lack reliabil-
ity and accuracy, the quality of treatment and clinical
research can be significantly compromised. If CFS is to
be diagnosed reliably across health care professionals, it
is imperative to provide specific thresholds and scoring
rules for the symptomatic criteria.
REFERENCES
[1] K. Fukuda, S. E. Straus, I. Hickie, M. C. Sharpe, J. G.
Donnibs and A. Komaroff, “The Chronic Fatigue Syn-
drome: A Comprehensive Approach to Its Definition and
Study,” Annals of Internal Medicine, Vol. 121, 1994, pp.
953-958.

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 15
[2] W. C. Reeves, D. Wagner, R. Nisenbaum, J. F. Jones, B.
Gurbaxani, L. Solomon, et al., “Chronic Fatigue Syn-
drome– A Clinically Empirical Approach to Its Definition
and Study,” BMC Medicine, Vol. 3, 2005. http://www.
biomedcentral.com/1741-7015/3/19
[3] J. E. Ware, K. K. Snow and M. Kosinski, “SF-36 Health
Survey: Manual and Interpretation Guide,” Quality Met-
ric Incorporated, 2000.
[4] E. M. Smets, B. J. Garssen, B. Bonke and J. C. DeHaes,
“The Multidimensional Fatigue Inventory (MFI): Psy-
chometric Properties of an Instrument to Assess Fatigue,”
Journal of Psychosomatic Research, Vol. 39, 1995, pp.
315-325.
[5] D. Wagner, R. Nisenbaum, C. Heim, J. F. Jones, E. R.
Unger and W. C. Reeves, “Psychometric Properties of the
CDC Symptom Inventory for Assessment of Chronic Fa-
tigue Syndrome,” Population Health Metrics, Vol. 3,
2005. http://www.pophealthmetrics.com/content/3/1/8
[6] L. A. Tiersky, R. J. Matheis, J. DeLuca, G. Lange and B.
H. Natelson, “Functional Status, Neuropsychological
Functioning, and Mood in Chronic Fatigue Syndrome,”
Journal of Nervous and Mental Disease, Vol. 191, 2003,
pp. 324-331.
[7] L. A. Jason, N. Najar, N. Porter and C. Reh, “Evaluating
the Centers for Disease Control’s Empirical Chronic Fa-
tigue Syndrome Case Definition,” Journal of Disability
Policy Studies, Vol. 20, 2009, pp. 93-100.
[8] L. A. Jason, M. Evans, M. Brown, N. Porter, A. Brown, J.
Hunnell, et al., “Fatigue Scales and Chronic Fatigue Syn-
drome: Issues of Sensitivity and Specificity,” Manuscript
Submitted for Publication, 2010.
[9] W. C. Reeves, B. M. Gurbaxani, J. S. Lin and E. R. Un-
ger, “A Response to Jason et al., ‘Evaluating the Centers
for Disease Control’s Empirical Chronic Fatigue Syn-
drome Case Definition,” Journal of Disability Policy
Studies Online First, 2009. http://dps.sagepub.com/cgi/
rapidpdf/1044207309346310v1.pdf
[10] C. Hawk, L. A. Jason and S. Torres-Harding, “Differen-
tial Diagnosis of Chronic Fatigue Syndrome and Major
Depressive Disorder,” International Journal of Behav-
ioral Medicine, Vol. 13, 2006, pp. 244-251.
[11] L. A. Jason, M. Brown, M. Evans, V. Anderson, A. Lerch,
A. Brown, et al., “Measuring Substantial Reduction in
Functioning with CFS,” Manuscript Submitted for Publi-
cation, 2010.
[12] L. A. Jason, K. M. Jordan, J. A. Richman, A. W. Rade-
maker, C. Huang, W. McCready, et al., “A Commu-
nity-Based Study of Prolonged and Chronic Fatigue,”
Journal of Health Psychology, Vol. 4, 1999, pp. 9-26.
[13] L. Kish, “Survey Sampling,” John Riley and Sons, In-
corporated, 1965.
[14] L. A. Jason, M. T. Ropacki, N. B. Santoro, J. A. Richman,
W. Heatherly, R. Taylor, et al., “A Screening Instrument
for Chronic Fatigue Syndrome: Reliability and Validity,”
Journal of Chronic Fatigue Syndrome, Vol. 3, No. 1,
1997, pp. 39-59.
[15] R. L Spitzer, J. B. W. Williams, M. Gibbon and M. B.
First, “Structured Clinical Interview for DSM-IV-
Non-Patient Edition (SCID-NP, Version 2.0),” American
Psychiatric Press, 1995.
[16] “Diagnostic and Statistical Manual of Mental Disorders,”
Forth Edition, American Psychiatric Association, 1994.
[17] M. B. First, R. L. Spitzer, M. Gibbon and J. B. W. Wil-
liams, “Structured Clinical Interview for DSM-IV Axis
I Disorders-Patient Edition,” Biometrics Research De-
partment, 1995.
[18] R. R. Taylor and L. A. Jason, “Comparing the DIS with
the SCID: Chronic Fatigue Syndrome and Psychiatric
Comorbidity,” Psychology and Health: The International
Review of Health Psychology, Vol. 13, 1998, pp. 1087-
1104.
[19] A. L. Komaroff and D. S. Buchwald, “Symptoms and
Signs of Chronic Fatigue Syndrome,” Reviews of Infec-
tious Diseases, Vol. 13, 1991, pp. 8-11.
[20] A. L. Komaroff, L. R. Fagioli, A. M. Geiger, T. H. Doo-
little, J. Lee, R. J. Kornish, et al., “An Examination of the
Working Case Definition of Chronic Fatigue Syndrome,”
The American Journal of Medicine, Vol. 100, 1996, pp.
56-64.
[21] D. Buchwald, R. Pascualy, C. Bombardier and P. Kith,
“Sleep Disorders in Patients with Chronic Fatigue,” Clin-
ical Infectious Diseases, Vol. 18, 1994, pp. S68-S72.
[22] A. L. Stewart, S. Greenfield, R. D. Hayes, K. Wells, W.
H. Rogers, S. D. Berry, et al., “Functional Status and
Well-Being of Patients with Chronic Conditions: Results
from the Medical Outcomes Study,” Journal of the
American Medical Association, Vol. 262, No. 7, 1989, pp.
907-913.
[23] P. Goodnick and R. Sandoval, “Treatment of Chronic
Fatigue Syndrome and Related Disorders: Immunological
Approaches,” In P. Goodnick and N. Klimas Eds.,
Chronic Fatigue and Related Immune Deficiency Syn-
dromes, American Psychiatric Press, 1993, pp. 131-161.
[24] L. A. Jason, N. Porter, J. Hunnell, A. Rademaker and J.
Richman, “CFS Prevalence and Risk Factors Over Time,”
Manuscript Submitted for Publication, 2010.
[25] W. C. Reeves, A. Lloyd, S. D. Vernon, N. Klimas, L. A.
Jason, G. Bleijenberg, et al., “Identification of Ambiguities in
the 1994 Chronic Fatigue Syndrome Research Case Defini-
tion and Recommendations for Resolution,” BMC Health
Servic es Resea rch, Vol. 3, 2003. http://www.biomedcentral.
com/content/pdf/1472-6963-3-25.pdf
[26] J. A. Hanley and B. J. McNeil, “The Meaning and Use of
the Area under a Receiver Operating Characteristics
(ROC) Curve,” Radiology, Vol. 143, 1982, pp. 29-36.
[27] K. H. Zou, A. J. O’Malley and L. Mauri, “Receiver-
Operating Characteristic Analysis for Evaluating Diag-
nostic Tests and Predictive Models,” Circulation, Vol.
115, 2007, pp. 654-657.
[28] M. H. Zweig and G. Campbell, “Receiver-Operating
Characteristic (ROC) Plots: A Fundamental Evaluation
Tool in Clinical Medicine,” Clinical Chemistry, Vol. 39,
1993, pp. 561-577.

Sensitivity and Specificity of the CDC Empirical Chronic Fatigue Syndrome Case Definition 16
[29] W. C. Reeves, J. J. Jones, E. Maloney, C. Heim, D. C.
Hoaglin, R. Boneva, et al., “New Study on the Preva-
lence of CFS in Metro, Urban and Rural Georgia Popu-
lations,” Population Health Metrics, Vol. 5, 2007. http://
www.pophealthmetrics.com/content/5/1/5
[30] M. Reyes, R. Nisenbaum, D. C. Hoaglin, E. R. Unger, C.
Emmons, B. Randall, et al., “Prevalence and Incidence of
Chronic Fatigue Syndrome in Wichita, Kansas,” Archives
of Internal Medicine, Vol. 163, 2003, pp. 1530-1536.
[31] L. A. Jason, J. A. Richman, A. W. Rademaker, K. M.
Jordan, A. V. Plioplys, R. Taylor, et al., “A Commu-
nity-Based Study of Chronic Fatigue Syndrome,” Ar-
chives of Internal Medicine, Vol. 159, 1999, pp. 2129-
2137.
[32] L. A. Jason, M. Evans, M. Brown and N. Porter, “What is
Fatigue? Pathological and Non-Pathological Fatigue,”
Manuscript Submitted for Publication, 2010.
[33] D. A. Regier, J. H. Boyd and J. D. Burke Jr., “One Month
Prevalence of Mental Disorders in the United States:
Based on Five Epidemiological Catchment Rea Sites,”
Archives of General Psychiatry, Vol. 45, 1988, pp. 977-
986.
[34] L. A. Jason, K. Corradi, S. Torres-Harding, R. R. Taylor
and C. King, “Chronic Fatigue Syndrome: The Need For
Subtypes,” Neuropsychology Review, Vol. 15, 2005, pp.
29-58.
[35] E. T. T. Jaynes, “Probability Theory: The Logic of Sci-
ence,” G. L. Bretthorst Ed., Cambridge University Press,
2003.
[36] C. King and L. A. Jason, “Improving the Diagnostic Cri-
teria and Procedures for Chronic Fatigue Syndrome,” Bi-
ological Psychology, Vol. 68, 2005, pp. 87-106.