ADHD symptoms in postsecondary 1 Running head: ADHD symptoms in postsecondary Higher Reported Levels of Depression, Stress, and Anxiety are Associated with Increased Endorsement of ADHD Symptoms in Postsecondary Students

This study examined the extent to which postsecondary students endorse symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) and whether experienced level of stress, depression or anxiety are associated with higher reporting of ADHD symptoms. Students attending a combined health and counseling service completed the Conners Adult ADHD Rating Scale (CAARS), the Depression Anxiety and Stress Scale (DASS) and the Life Experiences Survey. A subset also completed the Brown Attention Deficit Disorder Scale (BADDS). Findings demonstrate that the BADDS had weak specificity; 35% of never-diagnosed postsecondary students were classified as probably or very probably having ADHD based on their BAADS score. Lower false positive rates were found on the CAARS. Misdiagnosis of ADHD seems especially likely in students experiencing high levels of stress, depression, or anxiety, as these psychological conditions were associated with elevated reporting of ADHD symptoms; such conditions must therefore be considered when assessing for possible ADHD in young adults. ADHD symptoms in postsecondary 3 Introduction Diagnosing Attention Deficit/Hyperactivity Disorder (ADHD) in adults is challenging. Symptoms of this disorder, especially inattentive symptoms, are common to many disorders (Harrison, 2004; Weinstein, Staffelbach, & Biaggio, 2000). Although ADHD is said to be present in two to six percent of the adult population (Faraone & Biederman, 2005; Gallagher & Blader, 2001; Harrison, 2004; Weiss & Murray, 2003), it may be difficult to determine retrospectively whether a previously undiagnosed adult meets all of the childhood criteria for diagnosis of this disorder, as people do not always retain records of childhood behavior and/or such records may be unavailable later in life. Problems using checklists alone for diagnosis of ADHD Because there is no definitive test for ADHD, diagnosis often relies on checklists and self-report data (McCann & Roy-Byrne, 2004). This is problematic, as many of the symptoms listed in ADHD rating scales are present in a number of other disorders, or are symptoms which everyone experiences from time to time (Edmunds & Martsch-Litt, 2008) and it is more a matter of degree (severity, duration, and intensity) than actual presence of these symptoms alone that determines whether one is clinically impaired. For instance, individuals suffering from depression or anxiety frequently experience difficulty with memory, agitation, or inability to concentrate (APA, 2000) and stress can affect memory and cognition (Lupien, Fiocco, Wan, et al., 2005; McEwen & Sapolsky, 1995; Newcomer, Selke, Melson, et al., 1999). This is why the guidelines in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSMIV-TR; American Psychiatric Association [APA], 2000) require that other possible causes for the reported symptoms be investigated and ruled out prior to rendering a diagnosis of ADHD (APA, 2000). ADHD symptoms in postsecondary 4 Although the literature regarding ADHD assessment cautions that self-report checklists should never be the only data on which a diagnosis of ADHD is made, McCann and Roy-Byrne (2004) report that “many adults have been told that they have ADHD based largely (and sometimes solely) on their responses to self-report indices of symptoms” (p. 181), a finding that was reaffirmed by Gathje, Lewandowski and Gordon (2008). More recently, Joy, Julius, Akter and Baron (2010) demonstrated that the majority of graduate-level students applying for accommodation of ADHD provide inadequate documentation of their disability. In almost all cases, the reports based the diagnosis on only one piece of evidence, typically a self-report inventory score. These researchers reiterate the fact that the diagnosis of ADHD must be based on multiple sources of data and not simply current self-report. Two popular self-report checklists used to assist clinicians in identifying possible ADHD symptoms in adults are the Brown Attention Deficit Disorder Scale (BADDS; T. E. Brown, 1996) and the Conners Adult ADHD Rating Scale (CAARS; Conners, Erdhardt, & Sparrow, 1999). Many postsecondary students report symptoms of ADHD on such checklists [see Harrison, 2004 (re: BADDS); and Alexander and Harrison (in press, re: CAARS)], especially the inattentive symptoms, increasing the likelihood of misdiagnosis if self-report is used as the only diagnostic criterion. Symptom reporting in postsecondary students College life is stressful for many students (Howard, Schiraldi, Pineda, & Campanella, 2006; Martin, Cayanus, Weber, & Goodboy, 2006; Rodgers & Tennison, 2009), including those students with ADHD (T. E. Brown, 2003). Young adults attending college or university are at increased risk of becoming depressed, anxious, or stressed (Bayram & Bilgel, 2008; Howard et al., 2006; Wong, Cheung, Chan, Ma, & Tang, 2006) and this risk has been growing in recent ADHD symptoms in postsecondary 5 years (Eisenberg et al, 2007; Gentile et al., 2010). Since depression, anxiety, and stress all interfere with thinking, concentration and memory (e.g. Christopher & MacDonald, 2005), students with these symptoms might be misdiagnosed as having ADHD based solely on their responses to ADHD checklists. Studies have found that many non-treatment seeking university students frequently report difficulties similar to those found in ADHD (e.g. Gouvier, Uddo-Crane, and Brown, 1988). For instance, Wong, Regennitter, & Barrios (1994) found that student controls with no reported disabilities endorsed many symptoms associated with ADHD such as difficulty concentrating when reading (81.8%), tiring easily (63.6%), poor memory (46.6%), and impatience (62.5%). Harrison (2004) also found a large percentage of non-ADHD students attending health and counseling services scored in the highly probable range on a self-report measure of ADHD symptoms (the BADDS). Although research has found the CAARS able to distinguish well between adults known to have ADHD and a non-clinical control group (Gallagher & Blader, 2001), little research has been conducted to evaluate the ability of such measures to distinguish between those who have ADHD and those who experience memory and/or attention problems as a result of another separate psychological condition. What research there is does suggest that students with no history of ADHD but with current psychological difficulties are prone to endorsing many symptoms found on ADHD self-report inventories (e.g., Suhr, Hammers, Dobbins-Buckland, Zimak & Hughes, 2008), and in fact such students report symptoms at a rate comparable to those with bona fide ADHD. It therefore seems clear that symptoms found on ADHD checklists may be reported commonly by postsecondary-level students, and as such may not be specific to ADHD. ADHD symptoms in postsecondary 6 Purpose of the present study There is therefore a need for research to establish the specificity of such self-report inventories, especially for students experiencing symptoms of stress, anxiety and depression. As such, the purposes of the present study were as follows: 1) To examine the incidence of ADHD symptom endorsement in a non-diagnosed postsecondary sample when employing two popular self-report checklists (BADDS and CAARS). 2) To explore the relationship between level of reported stress, depression or anxiety and ADHD symptom endorsement in students with no formal history of ADHD diagnosis. 3) To examine the ability of transient factors such as stress, depression, anxiety and negative life events to predict the level of reported ADHD symptoms in an undergraduate population. We expected to find that more non-diagnosed students than expected (more than 6% of the sample) would score at or above levels said to be diagnostic of ADHD on both the BADDS and on the inattentive subscales of the CAARS. We expected that scores on the depression, anxiety and stress subscales of the Depression, Anxiety and Stress Scale (DASS) would be positively correlated with scores on both the BADDS and the CAARS, particularly the CAARS inattentive subscales, and that students reporting a higher number of negative life events would also report more symptoms of inattention on ADHD checklists. Finally, we predicted that elevated levels of stress, depression, anxiety and a higher number of negative life events could predict obtained scores on the DSM subscales and ADHD Index of the CAARS. ADHD symptoms in postsecondary 7 Method Participants Students (N = 107) attending appointments at the combined health and counselling services at a major Canadian university were recruited to participate in this ethics-approved study; participants were entered into a draw for a chance to win $100. The top six reasons for health services visits are: prescriptions for birth control; sexually transmitted infection checks; tuberculosis testing (required for students with placements in health-related settings); mental health; pap smears; and upper respiratory complaints (M. Condra [Director of Health and Counselling Service], personal communication, March 29, 2011). The top six counselling issues are: adjustment issues; stress and anxiety; low mood; relationships; cross-cultural issues; and eating/weight issues (M. Condra, personal communication, March 29, 2011). No students in the sample were attending regarding questions about possible ADHD. Materials Participants completed a questionnaire regarding medical history and other relevant demographic information and one or two measures of ADHD symptomatology: the Conners Adult ADHD Rating Scale (CAARS; Conners et al., 1999) and the Brown Attention Deficit Disorder Scale (BADDS; Brown, 1996). The BADDS data facilitated comparison with a previous study (Harrison, 2004), where a high level of false positive diagnosis using the BADDS was convincingly demonstrated in a postsecondary population. Due to technical difficulties obtaining the BADDS questionnaire during the first week of data collection, a smaller number of students completed this questionnaire. Students also completed the Depression, Anxiety and Stress Scale (DASS; Lovibond & Lovibond, 1995), and a measure of possible stressful life experiences, Life Experiences Survey ADHD symptoms in postsecondary 8 (LES; Sarason, Johnson, & Siegel, 1978). The questionnaires were presented in counterbalanced order to avoid answers to one questionnaire systematically influencing answers to the other questionnaires. The BADDS (Brown, 1996) is a 40-item self-report scale that focuses primarily on inattention rather than symptoms of hyperactivity and impulsivity (Roesler, Retz, Thorne, Schneider, Stieglitz, & Falkai, 2006). Respondents indicate their ability to organize and activate to work, sustain attention and concentration, sustain energy and effort, manage affective interference, and utilize working memory and access recall on a 4-point Likert scale (from 0 to 3). The BADDS test manual reports that “ADD” is probable for total scores in the 44-54 range and highly probable for scores greater than 54. The CAARS (Conners et al., 1999) is a 66-item scale that measures behaviors that are symptoms of ADHD using a 4-point Likert scale (from 0 to 3). This scale measures inattention/memory problems, hyperactivity/restlessness, impulsivity/emotional lability, and problems with self-concept and contains three Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR: American Psychological Association, 2000) ADHD symptoms subscales and an ADHD index. The ADHD index is a measure of the overall level of ADHDrelated symptoms and the test manual indicates that this index represents the best set of items for distinguishing ADHD adults from nonclinical adults. The test manual further asserts that one subscale T-score over 65 is said to indicate marginal support for a diagnosis of ADHD, with a greater number of subscale elevations signaling a higher likelihood of moderate to severe problems. According to Gallagher and Blader (2001), the CAARS is very useful for discriminating between adults with ADHD and adults who do not have clinical concerns. They state that the ADHD symptoms in postsecondary 9 CAARS is psychometrically sound and has an advantage over a scale like the BADDS because it has “established reliability with adults and has been normed on a larger sample” (p. 153). Erhardt et al. (1999) achieved an overall correct classification rate of 85% in ADHD and nonADHD adults (N of 79) using discriminant function analysis with the subscale scores of the CAARS. Another study found sensitivity of 71% and specificity of 75% for the ADHD index score (Conners et al. 1999). Erhardt et al. (1999), reports internal reliability of the factor scales as ranging between .86 and .92, and test-retest reliabilities as ranging between .88 and .91. Neither the CAARS nor the BADDS contains a symptom validity scale to evaluate the extent to which a respondent may be exaggerating or magnifying symptom report. The CAARS, however, does contain an Inconsistency Index which measures the extent to which the respondent endorsed items of similar content in a similar manner. The CAARS manual notes that scores of 8 or more on this index serve to invalidate the obtained results, as the individual has responded in too irregular a manner to validly interpret the data. The DASS (Lovibond & Lovibond, 1995) is a 42-item self-report scale that measures depression, anxiety, and stress using a 4-point Likert scale (from 0; does not apply to me, to 3: applies to me very much). When assessing the internal consistency of the DASS, Antony, Bieling, Cox, Enns, & Swinson, 1998 found Cronbach’s alphas for the DASS Depression, Anxiety, and Stress subscales to be .97, .92, and .95, respectively. For concurrent validity of the DASS, Antony et al. found correlation coefficients of .85 between the anxiety subscale of the DASS and the Beck Anxiety Inventory (BAI; Beck & Steer, 1990), and .77 between the depression subscale of the DASS and the Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979) among clinical patient groups. These same investigators also found the stress subscale of the DASS to correlate .62 with the BDI, .64 with the BAI, and .59 with the StateADHD symptoms in postsecondary 10 Trait Anxiety Inventory –Trait version (STAI-T; Spielberger, 1983). Test–retest reliability for the DASS within a clinical sample was found to be .71 for depression, .79 for anxiety and .81 for stress (T. A. Brown, Korotitsch, Chorpita, & Barlow, 1997). In order to avoid potentially spurious correlations, three items on the DASS that were very similar to items on the CAARS were initially removed from the DASS stress subscale and all analyses were performed with and without these items. No findings changed as a result of this analytical manipulation, so, for the sake of the reliability of the scales, both inventories were left intact. The LES (Sarason, Johnson, & Siegel, 1978) is a 60 item self-report measure that allows respondents to rate events they have experienced during the last year as having either a positive or negative impact on their life. Respondents rate each item on a 7-point scale (a rating of -3 would indicate an extremely negative impact. A rating of 0 suggests no impact either positive or negative. A rating of +3 would indicate an extremely positive impact). The LES provides both a positive and negative change score, and a total score. Test-retest reliability is said to be adequate (Sarason et al., 1978). The LES negative change scores are significantly related to depression, social non-conformity, and discomfort (Sarason et al., 1978; Shaw et al., 2000). Procedure All students arriving for an appointment at either health or counselling services during a 6 week period in the fall term were provided upon arrival with a letter inviting them to participate in this voluntary study. The majority of students (89 or 91%) were from health services and eight (9%) were from counselling services. Completed protocols were returned to a locked box at these locations. Six of the returned protocols were missing substantial data on the primary measures and were therefore removed from the sample. All students were asked whether they ADHD symptoms in postsecondary 11 had ever previously been diagnosed as having ADHD. Three students had been previously diagnosed with ADHD; these students were removed from subsequent analyses but their scores are referred to later for comparison purposes only. Another seven students reported that they had wondered if they might have ADHD at some time in their lives, but none of the seven had a current diagnosis so their data remained in the sample. Finally, an additional seven participants had extreme levels of response inconsistency on the CAARS (Consistency Index scores equal to or greater than 8), invalidating their results. Their CAARS scores are therefore reported both with and without the data from these students. Unless otherwise indicated, the reported analyses for the remaining data are based on the 98 remaining subjects, 26% of whom were male; three percent did not indicate their sex. The sample size for statistics involving the Life Experiences Survey was reduced due to improper completion by six students (n = 92). The mean age of the sample was 22.6 y (SD = 2.9, range 18.7 to 35.2 y), comprising mainly 2 to 4 year students. Due to difficulties with timely delivery of test material, only a subset of the students completed the BADDS (N = 57). Given that our main purpose was to examine the effects of other stresses on symptom reporting, and that Harrison (2004) had previously demonstrated that a high percentage of never-diagnosed students reported high levels of symptoms on the BADDS, it was felt that this number was still sufficient to allow for investigation of the effects of stress, anxiety and depression on BADDS symptom endorsement. Excluding students who were missing substantial data or who were previously diagnosed with ADHD from further analysis resulted in a sample size of 51 for the BADDS. Results BADDS scores ADHD symptoms in postsecondary 12 A large percentage (21.6%) of non-diagnosed students returned scores on the BADDS questionnaire indicating ADD is “highly probable”, a finding consistent with Harrison (2004). An additional 13.7% scored above the value identified in the test manual as indicating “probable” ADD (see Table 1). CAARS scores Standardized T-scores for subscales of the CAARS and the CAARS consistency score were calculated for each participant. No student scored above critical values (T > 65) on all eight subscales of the CAARS (Table 2); the percentage of students scoring above critical values on one, two or more of the subscales may be viewed in Table 3. In the total sample, 29.6% of students scored above the critical value on at least one subscale of the CAARS, and 19.8% scored above critical values on at least two of the subscales. Excluding those students whose inconsistency score on the CAARS was elevated, it was still the case that 28.6% of students scored above the critical value of 65 on at least one subscale of the CAARS and 16.5% scored above 65 on two or more subscales (Table 2). Finally, of the seven students with no diagnosis but who had previously wondered if they had ADHD, three scored above both critical values on two or more subscales of the CAARS, and remainder did not score above the critical values on any subscales. Other tests The majority of the students scored in the normal range on all three subscales of the DASS. The DASS Inventory defines the normal range for each subscale: Depression 0-9, Anxiety 0-7, & Stress 0-14. Mean scores for Depression (M = 8.27, SD = 9.46), Anxiety (M = 7.93, SD = 8.32) and Stress (M = 11.93, SD = 9.39) were all within the normal range, although the mean Anxiety ratings bordered on the mild range. Mean score for DASS Total was 28.17 ADHD symptoms in postsecondary 13 (SD = 24.01). Table 3 shows the percentage of respondents who scored above normal for each subscale of the DASS. Only negative change LES scores were calculated, and in general respondents reported relatively few recent negative life events (see Table 3). Associations among tests As expected, there is a strong association between the BADDS total and the CAARS ADHD Index, r = .83, p < .001. This association accounts for 68.89% of the variance in scores. Thus, the BADDS test is strongly but not perfectly associated with the CAARS self-report inventory. Table 4 shows the bivariate correlations among the inventories presented to participants, excluding seven participants with suspect CAARS consistency scores. BADDS scores increased proportionally with the total DASS score, accounting for 45.43% of the variance in total BADDS score. The DASS subscales independently showed positive correlations with the BADDS total score (all subscales p < .01); however, the negative LES scores did not correlate with the BADDS. The CAARS ADHD Index was positively associated with the DASS accounting for 31.70% of the variance. It is noteworthy that all of the participants scoring above T = 65 for the ADHD Index are within the top seven scores on the DASS. Separately, the DASS subscales also correlated with the ADHD Index. As shown on Table 4, only the CAARS subscales Problems with Self-concept and DSM-IV Hyperactivity Symptoms associated more with the DASS subscales (Depression and Stress, respectively) than with the DASS Total score. The negative LES score correlated only with the CAARS Problems with Self-concept subscale T-score, p < .01 (see Table 4). The more negative occurrences the student had experienced, the more they ADHD symptoms in postsecondary 14 report symptoms on this CAARS scale. When the DASS total and negative LES score were entered into a multiple regression sequentially, the LES score did not contribute to the model. The summary ADHD Index of the CAARS was significantly associated with all subscales of the DASS; and, we wanted to examine whether a substantial amount of variability in certain of the CAARS subscales could be predicted by total level of stress, depression and anxiety. In Table 5 we show only the subscales of the CAARS that had a higher than expected percentage of students (i.e., more than 6%) who scored above critical values, specifically: the Inattention/memory Problems subscale; Problems with Self-concept subscale; the DSM-IV Inattentive subscale; the DSM-IV Hyperactivity subscale; and the DSM-IV ADHD Symptoms Total subscale. Using the DASS total as the predictor variable, we regressed each of these CAARS subscales and found a significant amount of variance was accounted for by each relationship (see Table 5). Discussion This study examined ADHD symptoms when measured using two widely employed ADHD self-report checklists in a student group with no previous ADHD diagnosis and who were attending a health and counselling service for reasons unrelated to ADHD, and investigated whether higher levels of depression, anxiety, or stress were associated with higher checklist scores. As predicted, a large percentage of these students scored above critical values on both tests; in the case of the BADDS, this percentage ranged from 21 to 35%, although the CAARS ADHD Index was less likely to return false positive identification of ADHD (6.7%). It is unlikely that 21 to 35% of the student population attending health and counselling services suffer from undiagnosed ADHD, especially when prevalence of this disorder is estimated at 6% of the adult population in general (Harrison, 2004; Weiss & Murray, 2003) and only 1% to 3% of the ADHD symptoms in postsecondary 15 university student population (e.g. Javorsky & Gussin, 1994; Lee, Oakland, Jackson, and Glutting, 2008). We also investigated whether higher levels of depression, anxiety, or stress were associated with higher ADHD checklist scores. Both main indices of the ADHD checklists were correlated with the DASS, as were inattentive subscales of the CAARS. The high correlations between the DASS and the CAARS ADHD Index suggest that high scores on the BADDS or on two or more subscales of the CAARS are not specific to ADHD and that scores on these scales are highly correlated with greater experience of stress, depression, and anxiety. It is true that merely scoring above critical values on scales of an ADHD checklist is not the same as meeting all the DSM-IV criteria for diagnosis of ADHD and therefore comparing the percentage of our sample who scored above the critical values on an ADHD checklist to the expected percentage in the population may not be ideal. However, if, as McCann and Roy-Byrne (2004) assert, clinicians often rely primarily on self-report checklists to diagnose adult ADHD then these percentages suggest that many young adults could be inaccurately diagnosed with ADHD if all of the DSM-IV diagnostic criteria were not applied stringently. Conners et al. (1999) consider the ADHD index the best screen for ADHD risk and this is confirmed by the current study as only 6.7% of respondents had T scores above 65 on this measure. But it is also important to look at the pattern of high scores, as multiple scores above the critical value of 65 are also indicative of ADHD risk. In this study, 16.5% returned a T score above 65 on two or more scales. Thus, the ADHD index appeared to be relatively specific and only produced false positive identifications for the students who reported the highest levels of depression, anxiety and stress. However, it also failed to identify the three students previously diagnosed with ADHD, suggesting low sensitivity. ADHD symptoms in postsecondary 16 In accordance with our prediction, we found that scores on the DASS subscales were positively correlated with scores on both the BADDS and some of the subscales of the CAARS, particularly the inattentive subscales. Furthermore, the amount of stress, depression and anxiety experienced by a student was able to account for a significant portion of the variability in many of the obtained CAARS subscales. However, our final prediction regarding negative life events was not confirmed; there was no association between negative life events and the likelihood that they report symptoms of ADHD, especially inattention. As a developmental disorder, it is not expected that a large number of students would first demonstrate symptoms of ADHD at the college level. By contrast, it is true that the levels of stress, depression and anxiety amongst college students are increasing (Schwartz, 2006; Twenge et al., 2010). The DASS scores from students in the present study are consistent with data from American colleges (e.g. Eisenberg et al., 2007; Twenge et al., 2010) showing that approximately 16% of college students suffer from clinical depression, and 19% experience moderate or severe anxiety. Given the higher prevalence of mental health problems on campus relative to the prevalence rate of ADHD in the general population, one must certainly question whether any ADHD-like symptoms reported by a postsecondary student might be secondary to psychological as opposed to developmental causes. Researchers have cautioned that other disorders can also produce symptoms that may be mistaken for ADHD symptoms (e.g. obsessive compulsive disorder, Rucklidge & Tannock, 2000; sleep-disordered breathing, Chervin et al., 2006) lending support to Harrison and Wilson’s (2005) recommendation to educate clinicians about the possible confound and overlap of symptoms on ADHD checklists with other conditions, especially given that many physicians use self-report checklists as the sole method of diagnosing ADHD (Joy et al., 2010; McCann & RoyADHD symptoms in postsecondary 17 Byrne, 2004). Additionally, symptoms alone are not enough to understand a person’s level of adjustment or their need for service as it is possible for an individual to show many ADHD-type symptoms without significant impairment, or conversely, to show few ADHD symptoms and yet experience significant impairment (Gordon et al., 2006). While we are in agreement with Barkley and Brown (2008) that having a mood disorder does not preclude a diagnosis of ADHD, our findings also lead us to believe that it is important to rule out depression, anxiety and stress before diagnosing ADHD, in keeping with the DSM-IV criteria. One might argue that those who scored above published critical values on the self-report checklists represent undiagnosed cases of ADHD, and that their higher reports of depression, stress and anxiety are a byproduct of coping with undiagnosed ADHD. Certainly, some individuals with ADHD also suffer from co-morbid anxiety and or depression (Young, Toone, & Tyson, 2003), and so it may be that our findings simply reflect a result of having undiagnosed ADHD. While possible, it seems unlikely given the prevalence rates of ADHD (up to 6% in the general population and up to 3% in university populations). Hence, while it may be possible that some undiagnosed students were included in this undergraduate sample, it seems highly unlikely that between 11-23% (the percentages scoring above critical values on three and two subscales of the CAARS, respectively) or up to 35% (based on probable or highly probable scores from the BADDS) of the student population have this condition and yet have remained undiagnosed throughout their school years. Limitations and Directions for Future Research A major limitation of this study is that while there are significant correlations between the DASS subscales and the ADHD rating scale scores employed, the experimental design does not allow inference of causation; however, our aim was not to claim ADHD causes psychological ADHD symptoms in postsecondary 18 problems or vice versa. Rather, our aim was to show that there is an association between these scales such that one should not make a diagnosis of ADHD without first ruling out depression stress or anxiety as being the primary cause of the symptoms. Also worth considering is the fact that university students are not representative of the adult population in general, and that our sample included fewer men than women. We were not able to verify the diagnoses reported by three students, nor could we objectively determine whether any of the other participants met the diagnostic criteria for ADHD. As such, we could not compute sensitivity and specificity statistics. Future research should undertake such an evaluation. Although, the current research did not examine individuals with clinically diagnosed cases of depression or anxiety, it did examine different levels of these constructs along a continuum in a dimensional rather than a categorical manner. Future research should investigate groups of individuals with diagnosed anxiety disorders, depression or clinically significant stress to examine the association between level of symptom complaint and reporting of such ADHD symptoms. Conclusion This study adds to the growing body of literature cautioning clinicians to explore alternative explanations for reported symptoms of ADHD in young adults attending college or university, especially in cases where the individual could be experiencing elevated levels of stress, anxiety or depression. The BADDS appears to be particularly vulnerable to heightened symptom reporting in individuals who are experiencing such situationally related emotions, and as such a diagnosis of ADHD based on symptom reporting alone has a high chance of being inaccurate. Reliance on all of the DSM-IV criteria for diagnosis, including evidence of ADHD symptoms in postsecondary 19 symptoms and impairment in childhood and that other emotional or psychological difficulties cannot better explain current symptoms is recommended as the best way to minimize the possibility of a false positive diagnosis. ADHD symptoms in postsecondary 20 Funding This work was supported in part by the Ministry of Training, Colleges, and Universities of Ontario. ADHD symptoms in postsecondary 21 References Alexander, S. A. and Harrison, A.G. (in press). Cognitive responses to stress, depression, and anxiety and their relationship to ADHD symptoms in first year. Journal of Attention Disorders. American Psychiatric Association. 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