The prevalence of cognitive malingering in persons reporting exposure to occupational and environmental substances

NeuroToxicology 27 (2006) 940–950

The prevalence of cognitive malingering in persons reporting exposure to occupational and environmental substances Kevin W. Greve a,b,*, Kevin J. Bianchini a,b, F. William Black a,c, Matthew T. Heinly a,b, Jeffrey M. Love a,b, Douglas A. Swift d, Megan Ciota b a

Department of Psychology, University of New Orleans, New Orleans-Lakefront, New Orleans, LA 70148, United States b Jefferson Neurobehavioral Group, Metairie, LA, United States c Department of Psychiatry & Neurology, Tulane University School of Medicine, New Orleans, LA, United States d Occupational Medicine Clinic, Metairie, LA, United States Received 27 March 2006; accepted 29 June 2006 Available online 6 July 2006

Abstract Objective: Directly estimate the prevalence of cognitive malingering in persons claiming exposure to occupational and environmental substances. Methods: Retrospective review of 128 neuropsychological cases with financial incentive. Estimates were based on two methods: (1) clinical identification using the Slick, Sherman and Iverson criteria for malingered neurocognitive dysfunction (MND), and (2) statistical modeling based on patient performance on several individual psychometric indicators of malingering. Results: The prevalence based on the clinical method was 40%. The statistically based estimates ranged from 30% to more than 45% depending on model parameters. Different incentive parameters may influence prevalence. Conclusions: Cognitive malingering in toxic exposure is common and must be adequately addressed in the clinical neuropsychological assessment of toxic exposure and in research on its neurocognitive effects or findings will likely over-estimate the degree of cognitive impairment and related disability. # 2006 Elsevier Inc. All rights reserved. Keywords: Toxic exposure; Malingering; Neuropsychological assessment; Prevalence; Malingered neurocognitive dysfunction

Neurotoxic chemical exposure has received increased attention in the general public health arena. Previously it was presumed that neurotoxic chemical exposure was predominately a workplace health issue. Recent reports from numerous research and government sources, including the United States Environmental Protection Agency (EPA), have highlighted the large number of sources of neurotoxic chemical exposure to the general population (USEPA, 1998). With the increase in recognized sources of exposure has come an increase in alleged neuropathology ascribed to neurotoxic agents. A concomitant increase is now being reported in workers compensation and plaintiff-initiated litigation centered on alleged neurotoxic chemical exposure (Breton et al., 1993). Due to the often subtle nature of the neurobehavioral problems attributed to alleged workplace or environmental

* Corresponding author. Tel.: +1 504 280 6185; fax: +1 504 280 6049. E-mail address: [email protected] (K.W. Greve). 0161-813X/$ – see front matter # 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.neuro.2006.06.009

exposure to neurotoxic agents, the neuropsychological examination may serve as the primary source of evidence to support legal claims for financial compensation (Guilmettte et al., 1993; Hartman, 1995). However, a patient’s performance during a neuropsychological evaluation can be adversely affected by factors not necessarily associated with neurological damage and accurate evaluation relies on the patient exerting reasonable effort throughout the evaluation (Hawkins, 1994). Malingering, the intentional exaggeration or fabrication of illness or disability motivated by substantial external incentive (APAD, 2002; Bianchini et al., 2005; Slick et al., 1999), is a particular threat to the validity of neuropsychological test results (Beetar and Williams, 1995; Heubrock and Petermann, 1998). Therefore, if a patient has incentive to appear impaired, the possibility of malingering must be considered and objectively examined (Binder and Rohling, 1993). The risk of malingering is not small and malingering represents a substantial health care problem. A large number of Americans believe that purposeful misrepresentation of claims

K.W. Greve et al. / NeuroToxicology 27 (2006) 940–950

in the compensation system is acceptable (PAM, 1992, 1993). Covert video surveillance demonstrated evidence of malingering in 20% of patients with incentive who were undergoing pain treatment (Kay and Morris-Jones, 1998). In patients with pain, rates of malingering may range from 10% to approaching 40% (Fishbain et al., 1999; Mittenberg et al., 2002). Mittenberg et al. (2002) estimated the baserate of malingering in traumatic brain injury (TBI) to be between 30% and 40% based on survey of board-certified clinical neuropsychologists. Larrabee (2005) reviewed a number of studies of malingering test performance and found a similar rate. Bianchini et al. (in press) reported comparable rates of failure on malingering tests and other validity indicators and diagnosable malingering in TBI and found that rates varied with the magnitude of incentive. Overall, these studies suggest that the rate of malingering likely ranges from 20% to 40% across a wide range of conditions. In contrast to TBI, the problem of malingering has been relatively neglected in cases of alleged toxic exposure (Bianchini et al., 2003a). Recently, Bianchini et al. (2003a) demonstrated that malingering does occur in this context, illustrating the conservative application of empirically based detection techniques and their use within the Slick et al. system for the diagnosis of malingered neurocognitive dysfunction (MND), thus highlighting the problem. Failure to address malingering in the context of toxic exposure may therefore compromise clinical and research findings and make it difficult for clinicians, toxicologists, and neuropsychologists to mutually agree as to the presence, severity, and likely clinical course of reported neurobehavioral problems attributed to toxic chemical agents. Over 15 years ago Singer (1990) recommended the standard inclusion of malingering measures in the neuropsychological evaluation of neurotoxic exposure. However the problem of symptom exaggeration on clinical neuropsychological evaluations has been only marginally addressed in studies of neurotoxic agents, (Hartman, 1995; White et al., 1990) though this may be changing (van Hout et al., 2006). One consequence of the relative neglect of malingering in toxic exposure is the lack of information about its prevalence among compensation-seeking patients. Accurate estimates of the prevalence of MND are necessary to inform decisions regarding the probability of malingering in individual cases and to help assess the diagnostic accuracy of clinical indicators of malingering (Meehl and Rosen, 1955). Obtaining accurate estimates of prevalence may also have more general clinical and policy implications. Estimating the prevalence of a phenomenon like malingering is inherently difficult so good data in any clinical condition are rare, though in TBI it has been the topic of considerable discussion and some empirical work (see above). In contrast, the prevalence of malingering in cases of alleged toxic exposure has not often been discussed and has been even less frequently studied. Moreover, the accuracy of the prevalence estimates that do exist or which can be derived from existing research is open to question. Mittenberg et al.’s survey data suggest that the prevalence of malingering in alleged cases of neurotoxic chemical-related disease is about 30%. The work of van Houtand colleagues (van

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Hout et al., 2003, 2006) suggests a similar range. van Hout et al. (2003) reported that 46% of solvent exposure patients failed either the test of memory malingering (Tombaugh, 1996) (TOMM) or the Amsterdam short-term memory (Schmand et al., 1999) (ASTM) test at the published cutoffs, though only 18.6% failed both tests. van Hout et al. (2006) reported that 27% of their solvent exposed sample (which may have included patients reported in the 2003 study) demonstrated ‘‘insufficient effort’’ defined as failure both the TOMM and ASTM. An additional 45% of the sample demonstrated ‘‘dubious effort’’, though how this was operationalized was not described. The patients with either dubious or insufficient effort represented 44% of those patients with abnormal neurobehavioral test scores. Significantly more of the insufficient effort group (41%) had incentive compared to the sufficient effort group (17%). There is no information about the percentage of patients with incentive who showed insufficient effort. Bowler et al. (2006) reported a rate of 3% failure on the TOMM and/or they Rey 15Item Test in a sample of litigating welders claiming neurocognitive injury due to manganese exposure compared to 4% of their non-exposed controls. These findings appear to suggest the prevalence of malingering in some forms of toxic exposure could range from the upper teens to the mid-40% range. However, important methodological issues limit the validity of the van Hout et al. (2003, 2006) failure rates as estimates of malingering prevalence. First, these studies were not designed to estimate malingering prevalence so the samples are not representative of solvent exposed patients who present with disability claims. Their samples were very selected; patients with no evidence of exposure never reached the assessment stage. In the 2006 study van Hout et al. (2006) almost 25% failed to meet the entry criterion of substantial exposure plus relevant symptoms plus appropriate temporal relationship plus no other obvious cause. Second, van Hout et al. (2003) did not report the incentive status of their patients. van Hout et al. (2006) indicates that not all their patients had incentive. Thus, the significance of the failure rates reported in their samples for baserate estimation is not clear. It is also unclear what to make of Bowler et al.’s findings since their failure rate is lower than typically seen with the TOMM alone in compensation-seeking samples (e.g., 27% (Gervais et al., 2004) and 11% (Greve et al., 2006) and no different from their own no-incentive matched controls. In short, while it seems that the prevalence of malingering in toxic exposure may fall in a range consistent with the overall estimates of Mittenberg et al. (2002) estimates derived from the existing literature are not very precise and their accuracy is open to question. Thus, the purpose of this study was to extend the existing research by directly estimating the baserate of MND in reported toxic exposure through the careful examination of archival case files. Data were obtained from the files of 128 persons referred for neuropsychological evaluation related to alleged exposure to environmental and industrial substances over a period of about a dozen years. All had financial incentive, usually in the form of a workers compensation claim or personal injury law suit. Two methodologically distinct but somewhat overlapping

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approaches to identifying malingering in this sample were utilized. The first method, referred to as clinical malingering classification, applies the criteria for the diagnosis of MND published in 1999 by Slick et al. (1999). These criteria have been used extensively in research and clinical practice and have served as the method for operationalizing malingering in numerous published studies of malingering in TBI and chronic pain (Bianchini et al., 2004a,b, 2003b; Etherton et al., 2005a,b, 2006a, 2006b; Greve et al., 2002, 2003a,b; Heinly et al., 2005; Larrabee, 2003a) as well as toxic exposure (Bianchini et al., 2003a). The second method identifies MND in a statistical probabilistic manner based on the diagnostic accuracy or efficiency (sensitivity and specificity) of individual malingering indicators. This method models MND prevalence using parameter estimates derived from MND detection research in TBI. The approach is conservative because TBI samples included patients with objectively demonstrable brain damage. The overlap between the two methods occurs because the malingering indicators used in the statistical approach also contribute, partially but not necessarily exclusively, to a diagnosis of malingering within the Slick et al. system. However, the statistical method differs from the clinical method in that it does not identify individual malingers but estimates the proportion of the sample who would likely have been classified as malingering based on the Slick et al. (1999) system. The use of two complementary methods potentially provides some additional validation of the estimates. The overall rate of MND across the entire sample was estimated using both the clinical and statistical methods. The rate of MND was also be examined within specific subgroups of patients (workers compensation versus personal injury; State versus Federal workers compensation) using both methods. Previous research (Bianchini et al., in press) has shown higher rates of malingering test failure in Federal cases because of greater potential compensation compared to state cases. We are not aware of any comparisons between workers compensation and personal injury. The statistical method also allowed a comparison of rates of MND based on the different indicator types (forced-choice symptom validity tests; internal clinical indicators of cognitive malingering; validity indicators from self-report measures of psychopathology). This study thus offers unique information about the rate of malingering in cases of alleged toxic exposure and about factors that may influence that malingering rate. 1. Method 1.1. Participants Data were abstracted from the files of 128 persons with identifiable financial incentive referred for neuropsychological evaluation related to alleged exposure to environmental and industrial substances. All data were archival and were collected over the past dozen years in the course of the clinical psychology practice of two practices in a single southeastern metropolitan area. Eighty-nine percent were represented by an

Table 1 Sample demographic characteristics

Age Education Months since injury

N

Mean

S.D.

Minimum

Maximum

128 127 123

40.8 12.0 74.7

11.1 2.9 32.8

18 3 6

78 20 137

N

%

Gender Male Female

92 36

71.9 28.1

Race Caucasian African–American Hispanic Asian Native American Not indicated

86 32 0 2 1 7

67.2 25 0.0 1.6 0.8 5.5

Incentive type Disability Workers compensation Personal injury litigation Other

15 74 37 2

11.7 57.8 28.9 1.6

attorney; the representation status could not be determined from the existing records for four patients (3.1%). Almost 58% were exposed in the course of their employment (workers compensation claimants) while almost 29% were involved in personal injury litigation. Note that it is likely that some workers compensation claimants were also involved in exposure-related litigation outside the workers compensation system but related to their work exposures. The remainder of the patients were involved in state or federal disability claims or the specifics of their financial incentives could not be determined. Of the workers compensation claimants, 78.4% were covered under state workers compensation law while 21.6% were covered under Federal regulations. Table 1 presents the descriptive statistics for the sample as a whole and the nature of the financial incentive. Table 2 lists the substances to which they were exposed, if known. Numerous factors influence whether a given substance will produce adverse health effects including neurocognitive dysfunction. Among these are the specific properties of the substances themselves, the dose, duration of exposure, and the route by which the substance enters the body. In most of these cases, the chemical to which the person was exposed was unknown, the amount of exposure undetermined, and there was little or no objective evidence of pathology. Many times when the substance was identified, there was no evidence that the substance had neurotoxic properties or, in the case of substances whose neurotoxic effects were indirect (e.g., hypoxia from pulmonary damage after chlorine exposure), no evidence of the prerequisite pathology. Because some substances are known to cause neurological damage (e.g., solvents, carbon monoxide) the malingering test performance of all patients was compared to that of non-malingering samples which included patients with objectively documented brain damage. Thus, the risk of false positive classification

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Table 2 Substances and compounds to which patients claimed exposure 1 (1%) 1 (1%) 1 (1%) 2 (2%) 18(14%) 3 (2%) 1 (1%) 1 (1%) 9 (7%) 2 (2%) 1 (1%) 3 (2%) 2 (2%)

Acrythane (diethelene glycol, butyl ether, diutyle phthalate, ethylene glycol) Benzene Butane Carboline paint (epoxy resin, toluene, methyl N-amyl ketone, methyl isobutyl ketone, carbon black) Carbon monoxide Chlorine gas Clonazepam Dicyclopentadiene Fumes (diesel, exhaust, paint) Gasoline Helium Herbicide (naphthaline and other aromatics, ethylene dichloride, pendimthalin) Hydrochloric acid

1 (1%) 3 (2%) 1 (1%) 2 (2%) 4 (3%) 2 (2%) 2 (2%) 2 (2%) 8 (6%) 7 (5%) 4 (3%) 2 (2%) 2 (2%) 1 (1%) 5 (4%) 40 (31%)

Hydrogen chloride Hydrogen sulfide Ketamine Lead Lead paint (occupational exposure) Maleic anhydride Mercury Mold 1,1-thiobis[2-chloroethane](mustard gas) Nitrogen tetroxide Pesticide (unspecified) Phosgene gas Propane Styrene Toluene Unknown chemical and chemical mixtures

Percentages are rounded to the nearest whole number. Some patients claimed exposure to more than one substance so the sum of the percentages is greater than 100.

errors was reduced at the potential cost of missing some actual malingerers (false negative errors). 1.2. Malingering identification 1.2.1. Clinical classification method Patients were categorized on the basis of the Slick et al. criteria for malingered neurocognitive dysfunction (MND) using a diagnostic decision tree like that presented by Millis (2004). In determining the presence of MND, the case must be evaluated on the basis of four criteria: (A) presence of substantial external incentive; (B) evidence from neuropsychological testing; (C) evidence from self-report and, (D) behaviors meeting the necessary B and C criteria are not fully accounted for by psychiatric, neurological, or developmental factors. Using this system, all diagnoses of malingering require the presence of external incentive (Criterion A) plus Criterion B and/or C evidence as noted below. Criterion B behaviors are sufficient for a diagnosis of malingering on their own. Evidence from Criterion B may include discrepancies between test data and known patterns of brain functioning (B3), behavioral observations (B4), information from collaterals (B5), and documented history (B6). However, the most powerful Criterion B evidence is documentation of a negative response bias on the basis of performance on an SVT (e.g., Portland Digit Recognition Test [PDRT] or TOMM; see Bianchini et al., 2001 for others). Performance on a forced choice measure can indicate either ‘‘definite’’ response bias (B1: obtained score is significantly below chance at alpha <0.05, two-tailed) or ‘‘probable’’ response bias (B2: obtained score on a well-validated measure of response bias is in a range consistent with exaggeration or feigning). Other ‘‘malingering’’ tests and indices from standard clinical measures can also meet B2. Criterion C behaviors include discrepancies between selfreport and documented history (C1), known patterns of brain functioning (C2), behavioral observations (C3), and information from collaterals (C4). This criterion (C5) includes evidence of exaggeration or fabrication of psychological symptoms on

self-report measures with well-validated validity scales (e.g., Minnesota Multiphasic Personality Inventory-2, MMPI-2). Criterion C indicators are considered evidence of possible malingering but are insufficient on their own for a diagnosis of malingering. In the context of incentive (Criterion A) a B1 finding is sufficient for a diagnosis of ‘‘Definite MND’’. A diagnosis of ‘‘Probable MND’’ can be made with two types of Criterion B evidence or one type of Criterion B evidence and one or more types of Criterion C evidence along with Criterion A. Criterion C evidence is not sufficient for a diagnosis in the absence of Criterion B evidence. ‘‘Possible MND’’ is diagnosed when the criteria for ‘‘Probable MND’’ have been met but Criterion D factors are present. See Table 3 for a summary of this system. For purposes of this study, Criterion B2 could be met on the basis of a positive finding on any of the following: (1) the PDRT (Binder, 1993); (2) TOMM (Tombaugh, 1996); (3) word memory test (Green et al., 1996) (WMT); (4) computer assessment of response bias (Conder et al., 1992) (CARB); (5) Millis formula for the California verbal learning test (Millis et al., 1995) (CVLT); (6) CVLT model average (Millis and Volinsky, 2001). A positive finding was any score greater than or equal to the 1% false positive (FP) error level for the PDRT (Greve and Bianchini, in press), TOMM (Greve et al., 2006), and CVLT indicators (Curtis et al., 2006) based on the performance of all nonmalingering TBI patients. The published cutoffs were used for the WMT (Green et al., 1996) and the CARB (Conder et al., 1992). Criterion B3 (deviation from known patterns of brain functioning) was met on the basis a difference between Wechsler Memory Scale-Revised (WMS-R) General Memory > AttenAttention and Concentration (Mittenberg et al., 1993) at the 5% FP error level (Hilsabeck et al., 2003). Criterion B6 (test performance worse than documented history) was based on at least two of the following from the Wechsler Adult Intelligence Scale-III (WAIS-III): LetterNumber Sequencing, Digit Symbol-Coding, Arithmetic, Symbol Search; OR, Processing Speed (Index, PSI), Arithmetic, Letter-Number Sequencing. Cutoffs were at the 6% FP

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Table 3 Summary of the of Slick et al. (1999) criteria for malingered neurocognitive dysfunction A. Presence of substantial external incentive B. Evidence from neuropsychological testing 1. Definite negative response bias 2. Probable response bias 3. Discrepancy between test data and known patterns of brain functioning 4. Discrepancy between test data and observed behavior 5. Discrepancy between test data and reliable collateral reports 6. Discrepancy between test data and documented background history C. Evidence from self-report 1. Self-reported history is discrepant with documented history. 2. Self-reported symptoms are discrepant with known patterns of brain functioning 3. Self-reported symptoms are discrepant with behavioral observations 4. Self-reported symptoms are discrepant with information obtained from collateral informants 5. Evidence from exaggerated or fabricated psychological dysfunction D. Behaviors meeting necessary criteria from groups B and C are not fully accounted for by Psychiatric, Neurological, or Developmental factors Definite Probable Possible

Meets Criterion Meets Criterion Meets Criterion Meets Criterion Criterion D

A AND Criterion B1 AND Criterion D. A AND two or more B Criteria (excluding B1); or, meets one B Criterion (excluding B1) AND one or more C Criteria. D A AND one or more C Criteria but NOT Criterion D; or, meets all criteria for Definite or Probable but DOES NOT meet

level for moderate severe TBI for Digit Symbol and LetterNumber Sequencing and PSI and Symbol Search (Etherton et al., 2006b). Because of the nature of the distribution, the 3% FP level was used for Arithmetic (Etherton et al., 2006a). Criterion C5 could be met on the basis of MMPI-2 F (infrequency), Fb (infrequency-back), and Fake Bad Scale (FBS) scores. Cutoffs for F and Fb were at the 95% FP error level (Greve et al., in press) for all TBI patients; for FBS, the 1 % FP level was used. Specific cutoffs can be obtained from the authors on request. Using the Slick et al. system, patients were classified into one of three groups: Definite MND; Probable MND; Possible MND; not MND. In addition to patients meeting the criteria for Possible MND as described in Table 3, patients who met only one B criterion were also classified as possible. Consistent with the overall malingering literature, the Definite and Probable MND patients are all considered to be malingering and represent the basis of the prevalence calculation. The malingering detection literature combines these two groups for two reasons: (1) they are essentially indistinguishable in terms of overall malingering findings; and, (2) from a medicolegal standpoint (which is particularly relevant here) both meet the standard of ‘‘more probable than not’’ or ‘‘to a reasonable degree of scientific certainty’’. It is also important to recognize that the purpose of this study is to document the prevalence of diagnosable malingering. Technically, malingering cannot be ruled out in any case. Thus, it is possible that some malingerers failed detection and that the actual prevalence is higher than reported here. In contrast, the criteria for a malingering diagnosis and the very low false positive error rates associated with the cutoffs selected for individual indicators means that the probability that non-malingerers have been incorrectly classified is very low.

1.2.2. Statistical estimation method The baserate of malingering can be estimated statistically on the basis of sensitivity and specificity data from individual tests and indicators as long as those data are derived from wellcontrolled known-groups studies. Such well-controlled studies are available for a number of indicators included in the present data set. The following tests have published classification accuracies for a range of cutoffs derived from the performance of TBI patients including those with objectively documented brain pathology: PDRT (Greve and Bianchini, in press), TOMM (Greve et al., 2006), CVLT DFA and LR formulas (Curtis et al., 2006), WAIS Digit Span and RDS (Heinly et al., 2005), and MMPIF, Fb, and FBS (Greve et al., in press). Estimating the baserate in this way requires the use of positive and negative predictive power (+PP, PP, respectively). Predictive power is an index of the probability that test result is accurate (Hennekens and Buring, 1987). That is, +PP (measured: true positives divided by the sum of true positives and false positives) is the probability that a positive test result was produced by a person with the given condition (e.g., malingering). In contrast, PP (measured: true negatives divided by the sum of true negatives and false positives) is the probability that a negative finding was produced by a person without the condition. Predictive power integrates both sensitivity and specificity data. Moreover, when applied to the group of patients who were positive on a given test, +PP provides an index of the proportion of those persons who would likely meet the criteria for malingering upon which the original sensitivity and specificity data were based (Slick et al., 1999). Similarly, when PP is applied to the group of patients who were negative, then it reflects the proportion of that group who would not have met criteria for malingering. Subtracting this value from one leaves

K.W. Greve et al. / NeuroToxicology 27 (2006) 940–950

the proportion of persons who were negative on the test but who would have met criteria for malingering. When the values derived with +PP are combined with those derived from PP one has an estimated the number or proportion of true malingerers. The following equations summarize this process: positives  þPP ¼ true positives

(1)

negatives  PP ¼ true negatives

(2)

1  true negatives ¼ false negatives

(3)

true positives þ false negatives ¼ all malingerers

(4)

all malingerers ¼ baserate or prevalence in the sample N

(5)

The complication with this seemingly simple statistical approach is that the calculation of PP requires an explicit assumption about pre-test odds (Millis and Volinsky, 2001). As noted in the Introduction, the base rate of malingering in toxic exposure maybe greater than 40%. Larrabee has estimated the base rate of malingering in TBI to range from 30% to 40%. Similarly, Mittenberg et al.’s research suggests that the baserate of malingering across a range of relevant conditions including TBI, toxic exposure, and chronic pain to be between 20% and 40%. In personal injury and workers compensation cases overall it was about 30%. Bianchini et al. (in press) suggests that the baserate in TBI is likely to be in that range but under some incentive conditions may be as high as 50%. For purposes of this study, we have modeled the baserate of malingering in toxic exposure using three different pre-test odds (0.20, 0.30, 0.40) which span the range reported by Mittenberg et al. (2002). PP was estimated for all reported scores based on data cited in the studies noted above for cutoffs associated with a specificity of approximately 95%. Due to the features of some score distributions, cutoffs were not always available at exactly the 95% specificity level. The precise specificity and sensitivity reported for a given cut score in TBI were used in the calculation of PP. These values are listed in Table 4. The specific cutoffs can be obtained from the authors. 2. Results 2.1. Clinical classification method Of the 128 patients in the sample, eight lacked sufficient data for a malingering classification. Of the remaining 120, 48 (40.0%) met Slick et al. criteria for a diagnosis of at least Probable MND (Slick et al., 1999). Table 5A presents the proportions of patients falling into each malingering classification. MND status was not meaningfully correlated with age (r = 0.09, r2 = 0.01), education (r = 0.22, r2 = 0.05), or time since exposure (r = 15, r2 = 0.02). Significantly more males (44.0%) than females (30.6%) were diagnosed as malingering ( p < 0.001). Race (X2[d.f. = 4] = 3.83, p > 0.05) was not significantly associated with MND diagnosis. Table 5B shows that the rate of malingering among the workers compensation

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Table 4 Precise sensitivity and false-positive error rate in traumatic brain injury for each indicator at cutoffs approximating 95% specificity FP Forced-choice symptom validity tests Portland digit recognition test (Greve and Bianchini, in press) Easy 0.05 Hard 0.05 Total 0.05 Test of memory malingering(Greve et al., 2006) Trial 1 Trial 2 Retention

Sens

0.66 0.52 0.70

0.05 0.05 0.05

0.54 0.49 0.61

Internal validity indicators California verbal learning test (Curtis et al., 2006) Millis discriminant function score 0.05 Millis model average 0.05

0.31 0.55

Wechsler adult intelligence scale (Heinly et al., 2005) WAIS-III digit span scaled score 0.07 Reliable digit span test 0.04

0.36 0.39

Psychological test validity indicators Minnesota multiphasic personality inventory (Greve et al., in press) F (Infrequency) 0.04 0.30 Fb (Infrequency-back) 0.04 0.47 Fake bad scale (FBS) 0.03 0.41 FP, false positive error rate in traumatic brain injury; Sens, sensitivity to malingering in traumatic brain injury.

claimants was more than 10% greater than among personal injury litigants. This difference is statistically significant ( p < 0.001). Though of comparable magnitude, the MND prevalence difference between state workers compensation

Table 5 Proportions of the full sample meeting Slick et al. (1999) criteria for various malingering classifications and proportions of patients meeting criteria for at least probable MNP as a function of case type and workers compensation jurisdiction N

%

(A) Full sample Not malingering Possible malingering

32 40

26.7 33.3

All not MND Probable malingering Definite malingering

72 40 8

60.0 33.3 6.7

All MND

48

40.0

Total sample

(B) Subgroup Case type Workers compensation Personal injury Workers compensation jurisdiction State Federal

120

–

N

% MND

71 33

47.9 36.4

54 16

46.3 56.3

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claimants and those managed under Federal regulations was not statistically significant. This may be due to the very small size of the Federal sample and thus may not reflect a real lack of difference. Follow-up with a larger and more balanced sample is needed.

It should be noted that because of differences in test selection between the two practices and changes in the composition of the test battery within practices over time, not all patients received exactly the same tests. The number and percentage of patients who had scores on each test is reported in Table 6 along with the percentage of positive findings on each test score examined and the related prevalence estimate. Table 7A presents the positive finding and prevalence estimates averaged across all scores. As can be seen about 1/3 of this sample was positive on examined indicators. Estimated malingering prevalence rates ranged from 32% to 47%. The 95% confidence intervals around these means all contain 40%, the estimated malingering prevalence rate derived from the clinical diagnostic method. Table 7B presents hit rate and prevalence rate data averaged across types of indicators. These data suggest that all of the different indicator types produced comparable estimates of prevalence that did not appear to differ from the summary values. As with the rates of MND defined by the Slick et al. criteria, the statistical baserate estimates (see Table 8) were significantly higher in the workers compensation claimants compared to

Table 6 Rate of positive test result and estimated malingering prevalence for at the selected cutoff each indicator

Stand-alone indicator of response Portland digit recognition test Easy Hard Total Test of memory malingering Trial 1 Trial 2 Retention

0.20

0.30

0.40

61 60 61

42.6 33.3 45.9

37.7 30.0 39.3

39.3 35.0 41.0

49.2 45.0 50.8

71 71 61

31.0 29.6 37.7

29.6 29.6 34.4

33.8 33.8 37.7

43.7 43.7 45.9

86 127

28.8 37.9

27.1 34.5

35.6 37.9

47.5 48.3

22.8 26.7

26.7 26.0

32.6 32.3

43.0 43.3

Self-report indicator of response bias Minnesota multiphasic personality inventory F (infrequency) 113 24.8 F-back 97 26.8 Fake bad scale 100 45.0

Estimated prevalence 0.20

0.30

0.40

31.8 5.3 13

36.3 3.7 13

46.6 4.1 13

Stand-alone indicator of response bias Mean 36.7 33.4 S.D. 6.6 4.4 N 6 6

36.8 3.3 6

46.4 3.0 6

Internal indicator of response bias Mean 29.1 S.D. 6.4 N 4

28.6 4.0 4

34.6 2.7 4

45.5 2.7 4

Self-report indicator of response bias Mean 32.2 S.D. 11.1 N 3

32.8 8.0 3

37.8 6.2 3

48.5 7.4 3

Mean S.D. N

33.3 7.8 13

B

the personal injury litigants for all baserate models ( p < 0.01, h2 > 0.35). While the estimated prevalence of malingering in the Federal workers compensation claimants was numerically higher that those in the State claimants, the results were not statistically different. The differences among the statistical estimates (on average 6% points) were smaller than for the clinical diagnosis (about 10% points) particularly in the Jurisdiction comparison.

Prevalence

bias

Internal indicator of response bias California verbal learning test Millis formula 59 Average model 58 Digit span Reliable digit span Digit span scaled score

% Pos

% Positive

A

2.2. Statistical estimation method

N

Table 7 Mean rate of positive test result and malingering prevalence estimated from all indicators (A) and from specific indicator types (B)

27.4 28.9 42.0

34.5 34.0 45.0

45.1 43.3 57.0

Table 8 Average positive test rate and estimated prevalence rates (for three models using different pre-test odds) as a function of case type and workers compensation jurisdiction Subgroup examined

Case type Workers compensation Mean S.D. Personal injury Mean S.D.

% Positivea

0.20

0.30

0.40

38.1 8.8

35.2 6.0

38.9 4.3

49.9 4.7

23.4 9.9

26.8 6.1

32.0 5.3

41.1 5.7

33.7 5.3

37.8 4.6

48.4 4.7

38.9 10.3

42.5 8.9

50.6 8.0

Workers compensation jurisdiction State Mean 36.7 S.D. 8.3 Federal Mean S.D.

Estimated prevalence

43.1 14.3

a Percent positive at cutoffs associated with approximately 95% Specificity in traumatic brain injury.

K.W. Greve et al. / NeuroToxicology 27 (2006) 940–950

3. Discussion The prevalence of malingering based on both the clinical and statistical methods ranged from about 30% to more than 45%. The prevalence of diagnosable MND using the Slick et al. criteria as the basis for estimation was 40%. The rates of malingering observed in this study are consistent with previous estimates in this toxic exposure and with estimates in TBI. These data also suggest that malingering is more common in workers compensation than in personal injury litigation and in workers compensation cases handled under Federal regulations as opposed to State regulations. However, in the latter case the differences in this sample were small and not statistically significant. In any case, this study does provide estimates of the prevalence of malingering which, unlike previous studies, are based on the direct assessment of malingering in individuals and tied to a systematic approach to the diagnosis of malingering. In short, this study replicates and extends existing research. Nonetheless, the weakness inherent in this type of research and in this particular study need to be recognized. In general, the accuracy of estimates of prevalence are heavily dependent on sampling methodology and sample size and larger samples are more likely to more representative of the population of interest than smaller samples (Straus et al., 2005). The present sample was relatively small by epidemiological standards and some of the subsamples were extremely small (e.g., the Federal Workers compensation sample included only 16 patients). Therefore, the margin of error will tend to be larger. As to representativeness, the present sample reflects all the toxic exposure cases with incentive seen in two active neuropsychology practices in the New Orleans area over almost 15 years. It is arguable that this sample is fairly representative of the kinds of toxic exposure patients typically seen in the area. It is also important to note some of the limitations of the diagnostic methods used in this study. The Slick et al. criteria are designed to diagnose cognitive malingering. The application of those criteria to toxic exposure is conservative because test cutoffs were based on samples that included TBI patients with objectively demonstrable brain dysfunction and moderatesevere injuries. Thus, the rate of false positive error for any one indicator was low (usually 5% or less). Therefore, it is unlikely that persons who were not malingering were diagnosed as malingering because multiple positive findings were required for a diagnosis. The conservative nature of the estimates is even more evident in the statistical estimates where false positive and false negative error rates were considered. A more likely shortcoming of the clinical classification method is the possibility that persons who were malingering were not detected by current methods. Arguably, toxic exposure can produce illness and symptomology in addition to or instead of the cognitive problems on which Slick et al. (1999) have focused. In particular, physical illness (e.g., respiratory distress), pain-related problems, and reactive psychological symptoms may also occur and may also be intentionally exaggerated or fabricated. The clinical method used here is not designed to address those other domains. Thus, it is possible

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that malingering prevalence estimates derived using the Slick et al. criteria underestimate that actual rate of malingering in toxic exposure. Recently, Bianchini et al. (2005) proposed criteria for the diagnosis of malingered pain-related disability (MPRD) that explicitly took into account the multidimensional nature of the symptom presentation of patients with pain (cognitive, somatic, emotional). They defined malingered pain-related disability (MPRD) as the intentional exaggeration or fabrication of cognitive, emotional, behavioral, or physical dysfunction attributed to pain for the purposes of obtaining financial gain, to avoid work, or to obtain drugs. This system could reasonably be extended to patients alleging toxic exposure under the rubric of malingered injury or illness related disability. The question is the diagnosis of malingering in any condition is not whether a person is truly ill/injured or malingering; truly ill or injured persons can and do malinger (Bianchini et al., 2004b, 2003b; Franzen and Iverson, 1998; Greve et al., 2003b; Iverson, 2003). The question is whether the nature and severity of disability attributed to the illness or injury is inconsistent with what would be expected given the physical parameters of that illness or injury. The problem of diagnosing malingering outside the cognitive domain (especially of physical disability, less so emotional disability) is the relative lack of well-validated tools for detecting intentional exaggeration. Of particular value in toxic exposure might be pulmonary function tests, posturography, and some measures of physical capacity (e.g., grip strength and other components of functional capacity evaluations). These types of procedure are sensitive to volitional effort and may thus be capable of identifying non-physiological performance patterns including malingering (Allum and Shepard, 1999; Artuso et al., 2004; Fishbain et al., 1999; Gianoli et al., 2000; Greiffenstein et al., 1996; Mallinson and Longridge, 2005; Park, 1988; Schapmire et al., 2002). They may thus be capable of identifying intention efforts to appear disabled if validated appropriately (for discussions of this the validation methodology, see Bianchini et al., 2005; Greve and Bianchini, 2004; Larrabee, 2005). In any case, the point is that in the context of toxic exposure (as well as other potentially compensable conditions), methods of malingering detection that focus on cognition and the estimates of malingering which arise from them likely underestimate the actual rate of malingering since compensable disability can be caused by problems in other functional domains. The estimates reported here which here which were based on the Slick et al. criteria (i.e., the clinical classification method) are particularly vulnerable to this limitation because they are explicitly for the diagnosis of malingered neurocognitive dysfunction. On the other hand, while the statistical method was heavily weighted toward cognitive malingering indicators, it did include indicators of emotional exaggeration (MMPI scales F and Fb (Greene, 2000)) and of somatic exaggeration (MMPI FBS (Larrabee, 1998)). Thus, the statistical method is more likely to reflect the broad range of potentially malingered disability in toxic exposure than the clinical method. At the same time, the MMPI scales are indicators of over-reported or

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exaggerated symptoms (Greene, 2000). To the extent that symptom exaggeration is correlated with exaggerated disability behavior as measured by physical function tests, then the questionnaire results may provide an accurate estimate of exaggeration in the somatic domain. If not, then estimates will be less accurate. Estimates which include physical measures would help clarify this situation. Malingering is one source of what might be referred to as ‘‘excess disability’’, symptom magnification, or illness behavior but is not the only source. A number of psychosocial factors may also contribute to disability beyond what can be reasonably explained by genuine physical illness. Somatization is one such factor. Somatization refers to the how ‘‘certain patients use their physical symptoms as a way of dealing with, and communicating about, their emotional lives . . . in this type of symptom magnification, physical symptoms may be easier to accept as causing current unhappiness and discontent than admitting that some psychological reason is contributing to if (p. 204)’’ (Gatchel, 2004). In short, somatization reflects the expression of psychological problems manifested in physical symptoms and complaints, a tendency to complain of or develop physical symptoms and illness when under emotional stress. The importance of somatization and psychological complication has been described in chronic pain (Block et al., 2003; Linton, 2000) and is a high prevalence phenomenon in primary care (Fink et al., 2005a) and neurology practices (Fink et al., 2005b). In the context of toxic exposure, multiple chemical sensitivity may reflect somatization (Bailer et al., 2005). This paper has focused on the prevalence of conscious symptom exaggeration, malingering. The Slick et al. system serves to operationalize malingering and thus allows the differentiation of malingering versus other forms of exaggeration. Because of their practical importance (as noted in the preceding paragraph), it is also worth considering the influence of these other sources of symptom magnification and excess disability in toxic exposure patients. Examination of performance on the FBS scale may prove enlightening in this regard. FBS scores greater than 23 are believed to reflect exaggeration of symptoms beyond those which can be explained by genuine medical illness (Greiffenstein et al., in press). Three studies of FBS in TBI demonstrated that only 9% of TBI patients without incentive scored above 23 compared to 34% with incentive but without evidence of intentional exaggeration (Greve et al., in press; Larrabee, 2003b; Ross et al., 2004). In the present toxic exposure sample, 45% scored above 27, a level consistent with intentional exaggeration (Greve et al., in press). In contrast, 69% scored higher than 23. Thus, almost 70% of persons claiming health effects due to toxic exposure presented with exaggerated symptomology, with more than half due to malingering. One of the best methods for identifying psychological overlay, including somatization, is the MMPI2. MMPI-2 scales are useful for predicting outcome in spinal surgery (Block et al., 2003) and are associated with non-organic symptom responses on diskograms (Block et al., 1996). The findings from this study combined with existing literature on malingering and somatization demonstrate the importance of considering psychological factors in any person claiming

adverse health effects due to alleged toxic exposure. The less objective evidence of pathology present, the greater should be one’s concern about psychological involvement. 4. Summary The present study presented estimates of the prevalence of malingering in toxic exposure. While imperfect, this study is the first to use the direct assessment of malingering to provide the data from which the estimates were derived. The results of this study suggest that the prevalence of malingering in persons alleging toxic exposure is about 40%. The data suggest that different incentive parameters (e.g., the magnitude of potential compensation) may influence the rate of malingering. The rates of malingering observed in this study are consistent with the survey results of Mittenberg et al. (2002) and generally in line with the findings from TBI. Although rates of malingering may vary across samples, the results of this study suggest that group studies which report very low rates of malingering (e.g., less than 10%) are at least suspect in terms of the methodology employed for assessing malingering. For example with the Bowler et al. study on malingering mentioned above in which only 2 of 62 (3%) failed malingering tests. Overall, these results emphasize the fact that malingering in toxic exposure is not rare and that the failure to adequately address malingering in particular and exaggeration more generally in clinical neuropsychological assessment of toxic exposure cases and in research on the neurocognitive effects of toxic exposure will likely result in an over-estimate of the degree of impairment and related disability. Acknowledgments The authors would like to acknowledge the assistance of Elizabeth Uribe and Steven Springer. Jeffrey M. Love is now at Pennsylvania State University. References Allum JH, Shepard NT. An overview of the clinical use of dynamic posturography in the differential diagnosis of balance disorders. J Vestib Res 1999;9:223–52. American Psychiatric Association Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Association; 2002 [text revised]. Artuso A, Garozzo A, Contucci AM, Frenguelli A, Di Girolamo S. Role of dynamic posturography (Equitest) in the identification of feigned balance disturbances. Acta Otorhinolaryngol Ital 2004;24:8–12. Bailer J, Witthoft M, Paul C, Bayerl C, Rist F. Evidence for overlap between idiopathic environmental intolerance and somatoform disorders. Psychosom Med 2005;67:921–9. Beetar JT, Williams JM. Malingering response styles on the memory assessment scales and symptom validity tests. Arch Clin Neuropsychol 1995;10:57–72. Breton JJ, Valla JP, Lambert J. Industrial disaster and mental health of children and their parents. J Am Acad Child Adolesc Psychiatry 1993;32:438–45. Bianchini KJ, Mathias CW, Greve KW. Symptom validity testing: a critical review. Clin Neuropsychol 2001;15:19–45. Bianchini KJ, Houston RJ, Greve KW, Irvin TR, Black FW, Swift DA, et al. Malingered neurocognitive dysfunction in neurotoxic exposure: an application of the slick criteria. J Occup Environ Med 2003a;45:1087–99.

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