Routine data on early childhood from PCH services are predictive for ASD and ADHD problems in adolescents in the general population. The PCH services are a useful setting to identify high risk groups, and to monitor them subsequently.For clinically referred children with Autism Spectrum Disorder (ASD) or Attention Deficit/Hyperactivity Disorder (ADHD) several early indicators have been described. However, knowledge is lacking on early markers of less severe variants of ASD and ADHD from the general population. The aim of the present study is to identify early indicators of high risk groups for ASD and ADHD problems based on routine data from community pediatric services between infancy and age four. Data are from 1,816 participants who take part in Tracking Adolescents’ Individual Lives Survey (TRAILS), a longitudinal study. Information on early developmental factors was extracted from charts of routine Preventive Child Healthcare (PCH) visits. To assess ASD and ADHD problems, respectively, we used the Children’s Social Behavior Questionnaire (CSBQ) and the Child Behavior Checklist (CBCL), filled out by parents three times between the ages of 11 and 17. Note that these are parent ratings and not diagnostic instruments performed by trained clinicians. Male gender, low birth weight, low level of education of the mother, social, behavioral, language, psychomotor and eating problems significantly predicted ASD problems (odds ratios (OR) between 1.34 and 2.41). ADHD problems were also predicted by male gender and low level of education of the mother and by maternal smoking during pregnancy, good gross motor skills in first year Autistic spectrum disorders (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD) are neurodevelopmental disorders with an onset early in life, with a prevalence of around 1 % and 5 %, respectively  measurements, and from birth up to age four. The presence of ASD and ADHD problems was assessed during (pre-) adolescence. Note that previous studies on early indicators were focused on either ASD or ADHD. Given frequent co-occurrence  and ADHD problems  at T1. Both for ASD and ADHD problems, higher scores indicate more symptoms. The TRAILS study was approved by the national ethical committee ‘Central Committee on Research involving Human Subjects’.The TRacking Adolescents’ Individual Lives Survey (TRAILS) is a prospective cohort study among Dutch (pre-)adolescents, beginning at 10–12 years of age, that focuses on adolescent psychosocial development and mental health in the general population  and the Child Behavior Checklist (CBCL) at all three measurement waves. The CSBQ is a validated questionnaire for child social problems typically seen in children with ASD, especially in its milder forms, for ages 4–18  as part of the routine procedure of the PCH. These assessments included a general physical examination, standardized screening procedures and an interview with parents concerning health status and developmental  problems, which were all documented in the PCH file. An assessment takes approximately 10 to 15 min. In accordance with the literature we selected all potentially relevant predictors: pre- and perinatal factors, BMI and head circumference growth curves, psychomotor factors, early childhood behaviors, and sociodemographic variables from the PCH filePre- and perinatal variables concerned maternal smoking and alcohol use during pregnancy, low birth weight (registered in grams) and birth defects as provided by the obstetrician or midwife. Maternal smoking and alcohol use were assessed as: “Did the mother smoke during pregnancy?” and “Did the mother use alcohol during pregnancy?” These questions were part of the PCH files, but were also part of the T1 interview in TRAILS (when a child was age 11 years). If either answer was “yes,” we defined a score as indicative of smoking or alcohol use, respectively. The obstetrician or midwife could report yes or no, and in case of yes the amount per week or day. Often only yes or no was filled out, so more detailed information on quantity could not be retrieved. Low birth weight was defined as <2500 grams, which is the standard clinical cut-off point. Birth defects included limb deformities, craniofacial malformations and anomalies in organs, which are often seen in children with a clinical diagnosis of autism  was categorized into: low BMI, intermediate BMI and high BMI; gender and age-specific cut-offs were used , and middle and high  educational levels, respectively.th percentile. From that point onwards children began to have substantially more problems. For the CSBQ, the kink at the 80th percentile was at 9 for all three measurements; for the ADHD scale of the CBCL it was 7. We defined a score above the 80th percentile at least twice (out of three measurements) as indicative of the presence of ASD and ADHD problems. Note that these questionnaires were not diagnostic tools performed by trained clinicians.Prior to analyses, both outcome measures (CSBQ and CBCL) were dichotomized. To determine the optimal cut-off point for dichotomization for both questionnaires, we plotted the cumulative score distribution of decentile scores (x-axis) versus raw scores (y-axis). All graphs showed steep increases in problem behavior with a consistent kink at the 80p < 0.2 to be candidates for multiple logistic regression analyses. Variables were entered into a backward stepwise logistic regression procedure, generating a subset of independent predictors for the two outcome measures. As a final step, we investigated whether our final multivariate models changed when we brought the presence of ASD problems into the equation for predicting ADHD problems and vice versa; this to determine whether identified indicators were generic or specific to these problems. Statistical analyses were performed using SPSS Windows version 16.Subsequently we calculated descriptive statistics for all predictor variables and performed logistic regression analysis to examine the influence of early development on ASD and ADHD behaviors. Based on the results of the univariate analyses, we considered variables associated with the outcomes at Out of our overall sample , 348 adolescents met the ASD criterion and 419 fulfilled the ADHD criterion. One hundred and ninety-six met both ASD and ADHD criteria. Descriptive statistics of predictor variables in the ASD and ADHD groups, as well as in the remainder of the total sample, i.e. those who are not in either the ASD or the ADHD group, are presented in Table Table For ADHD problems on the CBCL, male gender, a low level of education of the mother, maternal smoking during pregnancy, gross motor skills (during the first year), attention hyperactivity problems, and absence of parent-reported positive behavior (at toddler age) were determinants. A low level of education of the father, sleep problems, problems with eating and externalizing problems did not contribute to the model as independent predictors in multivariate analyses. Controlling for ASD problems in the ADHD group left the model nearly intact; only maternal smoking during pregnancy and the absence of parent-reported positive behavior dropped below significance levels, with small reductions in ORs. The OR for the presence of ASD problems was substantial Table .This study was the first to use early childhood findings as registered by community pediatric services to predict ASD and ADHD problems in adolescents in a large longitudinal community-based sample. We identified several early childhood indicators predictive of ASD and ADHD problems. Male gender and low level of education of the mother were generic indicators, while a low birth weight, social behavioral problems, language, and psychomotor and eating problems at toddler age were specific for ASD problems, and maternal smoking during pregnancy, gross motor skills during infancy, and attention hyperactivity problems at toddler age, for ADHD problems.We found two important results that warrant further comment. Both controlling for ADHD problems in the ASD group and controlling for ASD problems for the ADHD group did leave the model virtually unchanged and vice versa, while the OR for the presence of ASD and ADHD problems was substantial. This indicates that neither risk indicators for ASD nor risk indicators for ADHD can be attributed solely to the shared variance between ADHD and ASD, therefore, early developmental risk indicators are to some extent specific for ASD and ADHD.A second intriguing result was that early attention and hyperactivity problems were predictive for later ADHD problems but not ASD problems while early social problems in behavior were predictive for later ASD problems but not ADHD problems. In contrast, during adolescence ADHD is predictive for ASD and vice versa  and psychosocial development of all children from birth on, in addition to screening for different disorders. Moreover, PCH services collect and register data according to a highly standardized format, including a number of objective parameters , therefore providing an outstanding setting for studying risk indicators in relatively unbiased general population samples.Given our focus on the general community, it is important to note that we identified largely the same early indicators as have been reported for referred and selected high risk ASD and ADHD samples, i.e. pre- and perinatal factors (Smidts and Oosterlaan, The major strength of our study is the uniqueness of its design: its prospective nature, spanning many years from birth on, and its embedding in a routine PCH setting. Another strength is that we studied ASD and ADHD problems in tandem, therefore the first to report on generic and specific early childhood indicators. A further strength is our use of multiple indicators of varying types, as previous ASD or ADHD studies only used subsets of indicators. Recent overviews have shown that in most prognostic studies single rather than multiple indicators are investigated, but that multiple indicators provide better models (Burton and Altman, , and also, as a result of the design of TRAILS, there were no cases of ASD with severe mental retardation in our study, while ASD problems are common in these children (de Bildt et al. Our study also has limitations, but insofar as these may have affected our results, they will all lead to underestimation of the predictive power of the PCH findings. First, children may have received effective treatment for their developmental problems between the ages of 4 and 11. Second, children in our sample had a lower mean score on ASD and ADHD problems than the remaining of the sample for whom PCH files could not be retrievedIn conclusion, the community pediatric services are a useful setting for both research on early predictors of ASD and ADHD problems in general population samples and for the early detection of children at risk in routine care. From early childhood PCH-registered routine data of a large community based sample we identified largely the same early indicators of ASD and ADHD problems as have been reported for clinically referred ASD and ADHD samples. This shows that routine PCH can identify children at risk of both mild and clinical ASD and ADHD in early childhood. The community pediatric services may also play an important role in close monitoring of children identified in such a way, by administering in depth diagnostic instruments to further qualify their symptoms, and by providing early treatment if needed. Notwithstanding relatively modest differences, this may have a rather huge public health impact as these services cover the full population of children Rose, .


ADHD is diagnosed and treated more often in males than in females. Research on gender differences suggests that girls may be consistently underidentified and underdiagnosed because of differences in the expression of the disorder among boys and girls. One aim of the present study was to assess in a clinical sample of medication naïve boys and girls with ADHD, whether there were significant gender x diagnosis interactions in co-existing symptom severity and executive function (EF) impairment. The second aim was to delineate specific symptom ratings and measures of EF that were most important in distinguishing ADHD from healthy controls (HC) of the same gender.Thirty-seven females with ADHD, 43 males with ADHD, 18 HC females and 32 HC males between 8 and 17 years were included. Co-existing symptoms were assessed with self-report scales and parent ratings. EF was assessed with parent ratings of executive skills in everyday situations (BRIEF), and neuropsychological tests. The three measurement domains  were investigated using analysis of variance (ANOVA) and random forest classification.ANOVAs revealed only one significant diagnosis x gender interaction, with higher rates of self-reported anxiety symptoms in females with ADHD. Random forest classification indicated that co-existing symptom ratings was substantially better in distinguishing subjects with ADHD from HC in females (93% accuracy) than in males (86% accuracy). The most important distinguishing variable was self-reported anxiety in females, and parent ratings of rule breaking in males. Parent ratings of EF skills were better in distinguishing subjects with ADHD from HC in males (96% accuracy) than in females (92% accuracy). Neuropsychological EF tests had only a modest ability to categorize subjects as ADHD or HC in males (73% accuracy) and females (79% accuracy).Our findings emphasize the combination of self-report and parent rating scales for the identification of different comorbid symptom expression in boys and girls already diagnosed with ADHD. Self-report scales may increase awareness of internalizing problems particularly salient in females with ADHD. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common childhood neuropsychiatric disorders, characterized by problems with inattention, hyperactivity and impulsivity . WorldwiAnother major contributing factor to late or missed diagnoses in females appears to be the presence of co-existing symptoms that often cloud the diagnostic picture . As manyWith regard to executive functions (EF), which are considered a central source of the disability associated with ADHD -19, prevIn sum, the gender gap in clinical populations of subjects with ADHD continues to hamper the correct diagnosis and treatment of females with ADHD. To our knowledge, the current study is the first to include both self-report and parent ratings of co-existing symptoms in addition to laboratory testing and inventory based scales assessing EF in medication naïve boys and girls with ADHD to examine potential gender sensitive ADHD profiles.Our first aim was to assess whether there were significant gender x diagnosis interactions in co-existing symptom severity and EF impairment. Using conventional ANOVAs we hypothesised that boys and girls with ADHD would show greater impairment in all three measurement domains  relative to HC. However, we expected to find few significant gender x diagnosis interactions in the three measurement domains. Our second aim was to delineate specific symptom ratings and measures of EF that were most important in distinguishing ADHD from HC in the same gender. We used random forest classification with cross-validation, where the identification of subtle differences across diagnosis and gender in moderately sized samples is possible. It was hypothesised that co-existing internalizing symptoms would better distinguish subjects with ADHD from HC in females than in males. Co-existing externalizing symptoms would better distinguish subjects with ADHD from HC in males than in females. Second, neuropsychological test results were expected to distinguish subjects with ADHD from HC equally well in males and females. Finally, parent ratings of EF were hypothesised to better distinguish subjects with ADHD from HC in males than in females.M = 11.2 years), 37 females with ADHD (M = 11.9 years), 32 healthy control (HC) males (M = 11.4 years) and 18 HC females (M = 11.9 years) between 8 and 17 years participated in the study. The ADHD participants were recruited as consecutive referrals from seven outpatient Child and Adolescent Mental Health Centres in Innlandet Hospital Trust (IHT) for assessment of ADHD. All participants underwent a comprehensive assessment according to common clinical practice. Semi-structured clinical interviews (Kiddie-Schedule for Affective Disorders and Schizophrenia - K-SADS) [Demographic characteristics are presented in Table  K-SADS)  were con K-SADS) , which cBased on diagnostic evaluation with K-SADS, co-existing diagnoses within the group of males with ADHD included depression (4.7%), anxiety (4.7%), conduct disorder (4.7%), and oppositional defiant disorder (11.6%). Co-existing diagnoses within the females with ADHD included anxiety (8.1%), and oppositional defiant disorder (10.8%). Despite a low prevalence of co-existing diagnoses, parent and self-report scales indicated elevated levels of externalizing and internalizing symptoms in both males and females with ADHD when compared with normal developing counterparts. Exclusion criteria for all participants included prematurity (< 36 weeks), IQ below 70, a history of stimulant treatment or any disease affecting the central nervous system. None of the participants used any type of psychopharmacological medication. One boy with ADHD was excluded due to estimated IQ below 70. None were excluded due to history of stimulant treatment or any disease affecting the central nervous system.F  = 4.60, p = .004, Eta2 = .099, and Bonferroni post-hoc analysis showed that both ADHD gender groups scored below the females in the HC group. On average, mothers of children in the HC group had 1.7 years more education than mothers of children with ADHD, F  = 6.80, p < 0.001. All parents/caregivers and participants above 12 years gave written informed consent in accordance with the Research Ethics Committee in Eastern Norway. All children under the age of 12 years provided oral consent to participate. The study was approved by the Regional Committee for Medical Research Ethics in Eastern Norway (REK-Øst), and by the Privacy protection ombudsman for research at Innlandet Hospital Trust. It was conducted in accordance with the Helsinki Declaration of the World Medical Association Assembly.All participants in the HC groups were screened for mental disorders with the K-SADS in separate interviews for children/adolescents and parents. The HC were recruited from local schools and were given a small compensation for participating. The HC could not have been treated for a mental disorder, have a psychiatric diagnosis, have had a head injury (with loss of consciousness) or known dyslexia. The four groups  did not differ significantly with regard to age and gender distribution. The Wechsler Abbreviated Scale of Intelligence (WASI)  was admiThe Child Behavior Checklist/6-18 (CBCL)  is a widThe Revised Children’s Manifest Anxiety Scale, second edition (RCMAS-2)  is a 49-The State-Trait Anxiety Inventory for Children (STAIC)  includesr = .67 and .51, respectively).The Short Mood and Feelings Questionnaire (SMFQ)  is a 13-The Letter-Number Sequencing Test (LN)  was usedThe Colour - Word Interference Test, Condition 3 (CW 3) ,56 was uThe Colour - Word Interference Test, Condition 4 (CW 4)  was usedThe Trail Making Test, condition 4 (TMT 4)  was usedThe Design Fluency Test, condition 3 (DF)  was usedThe Tower Test  was usedThe Letter Fluency Test (LF)  was usedThe BRIEF for children and adolescents aged 5 to 18 includes a parent form and a teacher form . In the Data analyses were conducted using the statistical package SPSS for Windows, version 15.0 . Demographic characteristics were investigated using the Chi-square test for independence  and analysis of variance (ANOVA) (continuous variables) followed up by Bonferroni post-hoc tests for group comparisons when adequate. ANOVAs were carried out to investigate gender x diagnosis interactions in the three measurement domains .randomForest 4.5-34 and caret 5.02-011) [In addition to tests of significance we also used an algorithmic modelling/data mining technique to explore gender differences in co-existing symptoms and EF ratings and measures. Classical statistical techniques are designed to test and reject the hypothesis that observed differences between groups have occurred by chance. Algorithmic modelling techniques have been developed to address a somewhat different question. Briefly, these techniques can identify from a sample of potential predictor variables the most important subset for categorizing subjects or predicting outcomes . Hence, .02-011)  to identBriefly, this is a form of “ensemble learning” in which a large number of unpruned decision trees are generated and their results aggregated . The ranEach decision tree was generated using results from 75% of the participants and then tested on the remaining 25% . This process was performed 5000 times on different random splits of the data to provide a cross-validated estimate of the predictive discriminant ability of the measures  that would likely generalize to new cases . The impThe first statistical approach tested the hypothesis that there were significant gender x diagnosis interactions across the array of dependent variables (DVs), viewing each of the DVs in isolation. In the second approach we evaluated the ability of ratings or measures in the: (1) symptom , (2) EF test performance, and (3) EF rating (BRIEF) clusters to predict whether participants met criteria for ADHD, and if the most important predictor variables in each cluster were the same for males and females.As seen in Table p < 10-8; kappa: z = 4.7, p < 10-5; two-sample Z-test). As seen in Figure s = 0.676, p < 0.003). CBCL ratings of social problems and affective problems were key distinguishing variables in both genders. However, rule breaking was the most important distinguishing variable in males, while physiological anxiety symptoms on the RCMAS-2 was the most important distinguishing variable in females.Random forest classification investigating the relationship between ADHD status and non-ADHD symptom cluster had a predictive  diagnostic accuracy of 0.860 ± 0.058 (mean ± SD) in males and 0.932 ± 0.055 in females. Kappa coefficients were 0.715 ± 0.115 and 0.844 ± 0.133 for males and females, respectively. This indicates that random forest classification using non-ADHD symptom ratings was substantially better at discriminating female subjects with ADHD from female HC than they were in discriminating males with ADHD from male HC . As seen in Figure s = 0.143, p > 0.7). The most important distinguishing variables in males were performance on tests assessing cognitive flexibility (DF) and verbal fluency (LF). The most important distinguishing variables in females were performance on tests assessing working memory (LN), inhibition (CW 3), and cognitive flexibility (CW 4).Random forest classification investigating the relationship between ADHD status and EF tests assessing: working memory, inhibition, cognitive flexibility, planning, and verbal fluency had only a modest ability to distinguish participants with ADHD from HC . There was a significant gender difference in the predictive ability of random forests based on measures of accuracy but not kappa . As seen in Figure s = 0.667, p = 0.07).Random forest classification investigating the relationship between ADHD status and BRIEF showed that parent ratings of executive skills were able to discriminate males with ADHD from male HC with high accuracy . Random forest classification using BRIEF items was not quite as accurate in discriminating females with ADHD from female HC , whereas the most important symptom in males was parent rated rule breaking . Elevated ratings of somatic complaints in girls with ADHD have previously been reported in population based studies , and supThird, random forest regression with cross-validation showed that neuropsychological measures of EF had only a modest ability to categorize participants as ADHD or HC. Differences in accuracy when categorizing participants as HC or ADHD in females versus males were slight (79% versus 73% accuracy), and were consistent with our hypothesis of no major difference in discriminatory power between genders. These results corroborate previous findings reporting moderate validity of EF tests for classifying children with ADHD ,23. InteFourth, random forest regression with cross-validation indicated that parental ratings of EF were relatively robust distinguishing variables of ADHD status in this sample. In line with our hypothesis, BRIEF scales was significantly better at discriminating males with ADHD from HC males than they were in discriminating females with ADHD from female HC. While there were differences in the relative importance of the different EF ratings, working memory appeared to be the most important distinguishing variable in both boys and girls. The sensitivity of working memory ratings in distinguishing boys and girls with ADHD from HC has been previously documented by Isquith & Gioia  and McCaIn our study, the participants’ symptoms were assessed with both self-report and parent rating scales. Previous research has shown that clinical samples of children often report more symptoms about themselves than parents do with regard to anxiety and depression -74. In cf = 0.1) with power of 0.8 would have required a much larger sample size (n = 787).Strengths of the present study are inclusion of subjects not medicated with psychopharmaca and no history of stimulant treatment. Additional strengths are the use of both self-report and parent ratings when assessing co-existing symptoms, and laboratory tests and inventory-based scales assessing EF. Further, random forest classification  is a relWe used random forests with cross-validation to simultaneously assess clusters of variables to delineate predictive accuracy and to identify the most important distinguishing variables. This approach has been shown to be effective in identifying the most important predictors in large sets of variables with fewer participants then can even be considered using conventional statistical techniques . For insThe present findings need to be interpreted in the context of some methodological limitations. Participants were recruited from a sample of referred subjects, and consequently are not necessarily representative of children and adolescents with ADHD in the general population. However, we believe the participants are fairly representative of males and females referred for the evaluation of ADHD related symptoms. Previous findings have reported that females with ADHD present more commonly with the inattentive subtype than do boys . These sOverall, females with ADHD could be more accurately distinguished from HC by the presence of co-existing symptoms, particularly physiological components of anxiety. On the other hand, parental reports of EF impairments were substantially better at distinguishing subjects with ADHD from HC in males. Given the almost universal phenomenon of “co-morbidity” in ADHD, our findings emphasize the combined value of self-report and parent rating scales for the identification of comorbid symptoms in boys and girls already diagnosed with ADHD.The authors declare no conflict of interest with respect to authorship or publication of this article.Parts of this paper were presented as a poster at the Eunethydis 2nd International ADHD Conference in Barcelona 23–25 May 2012.EWS managed the literature searches, undertook the preliminary statistical analyses, interpreted the data, and wrote the first draft of the current manuscript. MHT undertook the statistical analyses, helped interpret the data, and revised the current manuscript critically. PNA helped collect the data, and revised the current manuscript critically. KTH helped collect the data, and revised the manuscript critically. MØ wrote the protocol, managed the literature searches, helped interpret the data and revised the current manuscript critically. All consented to their names on the final manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/13/298/prepub


Over the last decade the use of psychotropic medications to treat common mental health problems has increased in Australia. This paper explores: 1) public attitudes towards the acceptability of using prescription drugs to treat depression and attention deficit hyperactivity disorder (ADHD), and 2) beliefs about over-diagnosis of depression and ADHD.1293 members of the general public were surveyed about their attitudes towards drug treatment for depression and ADHD through the Queensland Social Survey (QSS), an omnibus state-wide survey of households in the state of Queensland. The survey was administered through a CATI (computer-assisted telephone interviewing) system. Logistic regression analyses were used to predict belief that drug treatment is acceptable, and that depression and ADHD are over-diagnosed.Most participants (60.9%) said that it was acceptable to use prescription drugs to treat depression. In contrast, attitudes towards the use of prescription drugs to treat ADHD were much less positive with around the same proportion saying it was acceptable (42.1%) as unacceptable (38.2%). More than half of the sample agreed that too many people are diagnosed with depression when they don’t really have it (57.7%), and 78.3% of participants agreed that too many children are diagnosed with ADHD when they don’t really have it. Participants who said depression or ADHD were over-diagnosed were less likely to say that it is acceptable to treat these conditions with prescription drugs.Despite increases in prescribing rates there is still considerable scope for increasing the public’s acceptance of treating common mental illnesses with psychotropic drugs. Furthermore, the public’s views on over-diagnosis of depression and ADHD appear to reflect ongoing controversy about the proper identification of these conditions, and these views negatively impact attitudes towards drug treatment. This may be a barrier to effective treatment of these conditions given that drug treatment is often recommended as a first line response. Over the last decade the use of psychotropic medications to treat common mental health problems has increased in Australia. In particular, increases in prescribing rates for anti-depressants and stimulants  were among the highest for all psychotropic drugs. A recent overview of Australian prescribing trends showed that prescribing rates rose 95% for antidepressants and 72% for ADHD drugs between 2000 and 2011 . Table 3) Predicting the belief that depression is over-diagnosed. People who were not familiar with anyone who had undergone drug treatment for depression were much more likely to think that depression is over-diagnosed . Participants with 1–10 years of education were more likely to think depression is over-diagnosed compared to those with 13–14 and 15+ years of education (see Table 4) Over-diagnosis of ADHD. The overwhelming majority of participants agreed that too many children are diagnosed with ADHD when they don’t really have it (78.3%), and only 8.4% of participants disagreed. Among those who said ADHD is over-diagnosed, 41.5% said drug treatment for ADHD is acceptable, whereas 59.4% of participants who did not think ADHD is over-diagnosed said drug treatment is acceptable (see Table 1) Familiarity. Most participants (61.1%) did not know anyone who had been treated for ADHD with prescription drugs (“Not Familiar”). 35.3% knew someone who had been treated, and only 3.7% had been treated themselves. The ratio of agree/disagree responses was slightly higher for the two “Familiar” groups compared to those who did not know anyone treated for ADHD with drugs. Those in the “Not Familiar” group had the lowest overall rate of agreement with the acceptability of drug treatment for ADHD (36.5%), however a much higher proportion of people in the “Not Familiar” group also answered “Don’t know” (27.1%), compared to the two “Familiar” groups.2) Predicting positive attitudes towards the acceptability of drug treatment for ADHD. For ADHD, the full regression model was also statistically significant and able to distinguish between respondents who expressed outright agreement, and those who disagreed, with the use of drugs to treat ADHD, . Table 2  = 6.529, p = .588] (see Table 3) Prescriptions for anti-depressants and stimulants in Australia have increased at a higher rate than almost all other psychotropic medications over the last decade ,18,19. HThis study found that drug treatment for depression is acceptable to most members of the public approx. 60%), although almost 40% disagreed or were unsure. This result echoes our 2011 findings  need it. This may be for a number of reasons including concerns about unnecessary drug treatment and fear of being stigmatised by having a mental health problem. In such cases, perceptions about over-diagnosis and over-treatment could in fact result in some people in genuine need for help going untreated. One other potential implication is that members of the public who do receive a diagnosis of depression or ADHD may be more reluctant to initiate or continue drug treatments if they believe these conditions are typically over-diagnosed and over-medicated. This may be a barrier to effective treatment of these conditions given that drug treatment is often recommended as a first line response. It may be worthwhile for future research to explore how perceptions about diagnosis and treatment influence actual treatment seeking behaviour. For ADHD especially, it would be useful for future studies to explore in greater detail how attitudes towards stimulant prescribing are related to attitudes towards the diagnosis of ADHD, and to the use of stimulant medication. Parents of a child with ADHD may have concerns about the side-effects of stimulant treatment however these concerns may be amplified if they harbour doubts about the proper diagnosis of ADHD.Members of the Australian public in this study showed high levels of familiarity with drug treatment both conditions – around 67% of participants had either been treated for depression with prescription drugs or personally knew someone who had (65.4% in 2011). Approximately 35% knew someone who had been treated for ADHD with stimulants (28.7% in 2011). Our exploration of other factors predicting positive attitudes towards the acceptability of drug treatment yielded slightly different results to 2011. Knowing someone who had been treated for depression with prescription drugs (or having been treated oneself) was not a significant predictor of positive attitudes towards the acceptability of drug treatment for depression – unlike our 2011 survey. While our 2013 results showed a clear trend towards familiarity being a predictor, it did not reach statistical significance. Other studies with members of the Australian public have suggested that familiarity may be associated with more favourable attitudes towards drug treatment. For example there is evidence that Australians who know someone with depression are less likely to think anti-depressants are harmful . StudiesAlthough age was not a significant predictor of attitudes towards over-diagnosis, older people were much less likely to say that drug treatment for depression or ADHD is acceptable. This finding warrants further exploration given that older people were over-represented in this sample and participants over 55 years of age were more likely to answer “don’t know”. Compared to all other age categories, those aged 18–34 were far less likely to answer “don’t know”.Attitudinal surveys can provide a useful snapshot of the public viewpoints. The usefulness and robustness of their findings can be increased if they are repeated. Although there has been an increase in the prescription of drugs to treat depression and ADHD, the findings from the 2013 QSS and 2011 QSS indicate that there is still considerable scope for convincing many members of the public that drug treatment is acceptable or warranted. Addressing the belief that these disorders are over-diagnosed appears to be one part of this. This survey could be conducted again in the future to essentially track attitudes towards these issues over time and further elucidate the factors that influence attitudes. The major limitation of using the sample described in this study is that younger people are under-represented. While this is not unusual for CATI surveys with the general community, it does mean that it would be prudent not to overgeneralise these findings. Under representation of younger people often occurs in CATI surveys that source landline phones only, and dual frame (landline and mobile) surveys may improve the representativeness of future surveys. Nevertheless the survey tool could easily be used with other representative cohorts for direct comparisons or extended to explore other domains relevant to public attitudes towards the acceptability of drug treatment . This would potentially improve the explanatory power of the survey tool by taking into account a larger number of variables that inform attitudes. The survey could also be adapted to investigate attitudes towards the acceptability of drug treatment for other common mental illnesses such as anxiety and various substance use disorders. This would provide a broader understanding of the spectrum of public attitudes towards pharmacological drug treatment. Elsewhere, we have used a variation of this survey to explore attitudes towards the acceptability of healthy people using prescription drugs for “cognitive enhancement” .and teacher data to inform an ADHD diagnosis, or restricting the diagnosis to major types of impairment) and to avoid overtreatment .This paper demonstrates the importance of understanding public attitudes towards treatments for disorders such as depression and ADHD. Most members of the public thought depression and ADHD were over-diagnosed, although they were more accepting of drug treatment for depression compared to ADHD. Increases in the prescription of psychotropic medication may not result in better treatment if there are widespread public attitudes that do not support the use of drug treatment for these conditions. Further research about public attitudes may provide helpful insights into ways in which treatment for depression and ADHD may be better managed. In particular, it would be worthwhile investigating what members of the public and/or health care professionals think should be done to avoid over-diagnosis (for example, requiring parent This research was supported by a University of Queensland internal staff research grant awarded to Brad Partridge. Brad Partridge has been awarded an NHMRC post-doctoral fellowship. Wayne Hall has been awarded an NHMRC Australia Fellowship.BP, JL, and WH contributed to the design of the study and creation of the survey items. BP analysed the data. BP, JL, and WH contributed to the drafting of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/14/74/prepub


Attention deficit hyperactivity disorder (ADHD) is increasingly recognized as a common disorder not only in children, but also in the adult population. Similarly, asthma also has a substantial prevalence among adults. Previous studies concerning a potential relationship between ADHD and asthma have not presented consistent results.A cross-sectional study of 594 adult patients diagnosed with ADHD, compared with 719 persons from the general population. Information was collected between 1997 and 2005 using auto-questionnaires rating past and present symptoms of ADHD, co-morbid conditions, including asthma, and work status.The prevalence of asthma was significantly higher in the ADHD patient group compared to the controls, 24.4% vs. 11.3% respectively , and controls with asthma scored higher on ratings of both past and present symptoms of ADHD. Female ADHD patients had a significantly higher prevalence of asthma compared to male ADHD patients , but in controls a slight female preponderance was not statistically significant. In both ADHD patients and controls, having asthma was associated with an increased prevalence of symptoms of mood- and anxiety disorders.The present findings point to a co-morbidity of ADHD and asthma, and these patients may represent a clinical and biological subgroup of adult patients with ADHD. Attention deficit hyperactivity disorder (ADHD) is a common disorder in children causing substantial problems for those afflicted -3. DurinLike ADHD asthma is also perceived as a disease of childhood, however with a significant adult prevalence and incidence -14. BothThe pathophysiology and genetics of ADHD probably involve multiple neurotransmitter systems, including dopaminergic mechanisms , but a cPrevious studies that have examined the potential relationship between ADHD and asthma have not presented consistent results. Both a study of 140 boys with ADHD  and a stIn a study using data from the Norwegian Prescription Database we have shown that patients prescribed drugs to treat ADHD also are prescribed anti-asthma drugs significantly more often than the rest of the population .The aims of the present study were to (1) investigate the prevalence of asthma among clinically diagnosed adults with ADHD, compared to controls from a normal population, and to (2) investigate if the presence of asthma is associated with differences in symptom patterns and demographic variables in patients and controls.This is a cross-sectional study of 594 Norwegian patients diagnosed with adult ADHD and a comparison group of 719 persons from the general population. The patients were recruited as part of a genetic study using a national registry of adults diagnosed with ADHD in Norway during 1997-May 2005 ,21,28,29To enhance recruitment and to include patients diagnosed also later than May 2005, psychiatrists and psychologists nation-wide were invited to recruit formally diagnosed adult patients with ADHD. The inclusion criteria were a diagnosis of ADHD according to the criteria described above, and age above 18 years. There were no formal exclusion criteria. In the present paper we report on data from a total of 594 patients, 340 from the national registry, and 254 from the recruitment performed after May 2005.The control group was recruited using the database of The Medical Birth Registry of Norway (MBRN). The MBRN includes all people born in Norway after January 1st 1967. Invitation letters were sent out in 2007-2009 to a randomly selected sample of persons between 18-40 years from all over Norway. Data from the first 719 persons recruited are presented in the present report. For further details about the recruitment strategy and the patient sample, see Johansson et al. 2008 , HallelaInformed consent based on detailed written information about the project was obtained from all patients and controls. The study was approved by the Regional Research Ethical Committee of Western Norway.The following self-report questionnaires were used in this study: The Wender Utah Rating Scale (WURS), measuring the presence and frequency of childhood ADHD symptoms , the AduThe WURS is designed to retrospectively record symptoms and signs of ADHD in childhood. The version of the scale used in this study contains 25 questions, each rated on a 5-point severity scale. The WURS-25 has been validated by several investigators in different countries and populations ,35.The ASRS is the World Health Organization's (WHO) rating scale for adult ADHD designed to measure current ADHD symptoms. It consists of 18 items based on DSM-IV symptoms/criteria for ADHD that are measured on a 5-point scale (0 = never/seldom and 4 = very often), yielding a possible score range from 0-72. The items 1-9 cover the symptoms of inattention; items 10-18 the symptoms of hyperactivity and impulsivity. In this study we used a continuous scoring method .The MDQ is a screening instrument for BSD that has been validated for use in the general population and in psychiatric patient populations ,36. The In addition, self-reported data were collected concerning socio-demographic and clinical factors including educational and occupational levels and co-morbid symptoms and problems. A diagnosis of asthma was defined as answering yes to the following question: "Have you ever had asthma?"Bivariate associations were analysed using chi-square tests, t-tests for independent samples, and logistic regression analyses. Multivariable associations between ADHD and asthma were examined using a logistic regression model with asthma as the outcome variable, ADHD main predictor, and possible confounders age, gender, education, anxiety/depression and bipolar disorder. All analyses were performed using the Statistical Package for Social Sciences (SPSS) version 15.0.1.Clinical and socio-demographic characteristics of ADHD patients and controls are shown in Table A total of 143 of the ADHD patients reported that they had asthma 24.4%), compared with 81 (11.3%) of the controls . Even after controlling for age, gender, education, anxiety/depression, and bipolar disorder in a logistic regression analysis, the OR only decreased slightly, and retained statistical significance (Table 4.4%, comTable The characteristics of controls with and without asthma are shown in Table There are three main findings of the present study. The first is that adult patients with ADHD significantly more often reported a history of asthma, compared to a control population. The second is that controls with self-reported asthma reported more symptoms of ADHD both in childhood and currently, compared to controls without asthma. Finally, asthma in controls and in male ADHD-patients was associated with self-reported depression and/or anxiety.The ADHD patients in the present study are very impaired as a group, with a low level of education compared to controls, and less than one third being employed in ordinary work. This is in accordance with previous studies showing a low level of occupational functioning in adult patients with persistent ADHD ,11. HoweFemales in our control group had a slightly higher prevalence of asthma compared to male controls, but the difference was not statistically significant. Whereas childhood asthma is more common in boys, adult asthma is consistently more prevalent in females -39, possIn the previous clinical studies on the relationship between ADHD and asthma where no association was found ,24, chilSuch a co-morbidity may appear counterintuitive. ADHD and asthma are very different disorders. ADHD is a chronic disorder comprising problems with attention and concentration, combined with behavioural symptoms such as hyperactivity/restlessness and impulsivity . Asthma Much of the current thinking on the pathophysiology and genetics of ADHD has focused on alterations in dopaminergic systems , and theOther relevant factors that could help to explain this co-morbidity may be due to risk behaviour associated with ADHD, most notably tobacco smoking. Teenage and adult patients with ADHD have a higher prevalence of smoking in comparison with the general population . It is sAnother possibility is that inflammatory mechanisms may be a common factor for these disorders. Such mechanisms are important in the pathophysiology of asthma ,55, are Both ADHD and asthma are associated with obesity. Several studies have indicated a higher than expected prevalence of obesity in ADHD patients , and obeConcerning limitations it is evident that we are not studying the whole range of ADHD patients. Not all patients with such problems consult a doctor, and those that are recruited to the present study probably represent a more severely affected group . It is tThe diagnosis of asthma was made on the basis of "yes-no" answers to a questionnaire. We made no qualification that the diagnosis should have been given by a doctor. In a study from Germany a fairly good agreement was found between answers to such a question compared to a subsequent interview by a physician . FurtherIn conclusion, we have shown that adults patients with persistent ADHD have an increased prevalence of asthma compared to controls from the general population, and that controls with asthma report higher levels of both childhood and current ADHD symptoms. This points to a co-morbidity between these two disorders, possibly related to shared risk factors, pathophysiologies and co-morbidities with mood and anxiety disorders. We suggest that future studies should explore underlying pathophysiological mechanisms that may explain the co-occurrence of ADHD and asthma.During the past three years JH has been invited as a lecturer by Janssen-Cilag and Novartis, AH by Janssen-Cilag and OBF by Bristol-Meyers Squibb. TME has received an unrestricted grant from AstraZeneca in 2008, and travel support to attend the American Thoracic Society congresses in 2008 and 2011 from GlaxoSmithKline. KJO declare that he has no competing interests.JH, AH and OBF participated in the design of the study. AH collected and plotted data. OBF performed the statistical analyzes. OBF, JH, TME and KJO drafted the manuscript. All authors contributed to the interpretation of data and revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/11/128/prepub


Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by problems in social behaviour, which are sometimes similar to some symptoms of autism-spectrum disorders (ASD). However, neuronal mechanisms of ASD-like deficits in ADHD have rarely been studied. The processing of biological motion–recently discussed as a marker of social cognition–was found to be disrupted in ASD in several studies. Thus in the present study we tested if biological motion processing is disrupted in ADHD. We used 64-channel EEG and spatio-temporal source analysis to assess event-related potentials associated with human motion processing in 21 children and adolescents with ADHD and 21 matched typically developing controls. On the behavioural level, all subjects were able to differentiate between human and scrambled motion. But in response to both scrambled and biological motion, the N200 amplitude was decreased in subjects with ADHD. After a spatio-temporal dipole analysis, a human motion specific activation was observable in occipital-temporal regions with a reduced and more diffuse activation in ADHD subjects. These results point towards neuronal determined alterations in the processing of biological motion in ADHD. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common disorders in child and adolescent psychiatry and shows a prevalence of about 5% in the general population Up to date, only a few experimental studies about social cognition deficits in ADHD can be found , has recently been discussed as a marker of social cognition Biological motion processing can also be studied with electroencephalography (EEG). In contrast to fMRI, EEG can differentiate time related sub-processes of visual processing. Regarding biological motion, first, a positive component 100 ms after stimulus onset can be observed at occipital electrodes, especially in the right hemisphere In the present study, we tested if biological motion processing is disturbed in children and adolescents with ADHD. To our knowledge there are no studies about biological motion processing or perception in ADHD. Therefore we assessed event-related potentials (ERPs) elicited by biological and scrambled motion to test if biological-motion processing is abnormal in these participants. More elevated ASD-symptoms and thus more social deficits were previously found in children with ADHD and comorbid disruptive behaviour disorders We included 48 children and adolescents in our study. Two participants had to be excluded because they showed too few responses during the experiment , and four because of muscle artefacts in the EEG data (two in each group). The remaining 21 typically developing controls and 21 children and adolescents with ADHD were 7 to 15 years old see . Only maADHD out-patients were diagnosed according to ICD-10 F90.0;  at the DTypically developing controls were recruited from local schools and screened with the German version of the Child Behaviour Checklist Exclusion criteria for both groups were as follows: intellectual disability according to a standardized IQ assessment , any neurological disorders (including epilepsy), preterm birth with low birth weight (<2000 g), and dyslexia. In children with ADHD, comorbid psychiatric disorders  were excluded with the exception of oppositional defiant disorder (ODD) and conduct disorder (CD). Nine boys with ADHD and comorbid ODD or CD (ICD-10: F90.1) were included.Subjects received a small fee for their participation. All participants and parents signed informed consent. The study was approved by the Ethics Committee of the Goethe University Frankfurt (Germany) in accordance with the Declaration of Helsinki.IQ was assessed using the standard (over 11.5 years) or coloured (under 11.5 years) progressive matrices Autistic symptoms were assessed by the German version of the Social Communication Questionnaire SCQ;  in all pIn ADHD subjects, ADHD symptoms were assessed with a parent rating scale taken from a German diagnostic system for mental disorders in children and adolescents . The walkers were shown in a frontal view walking with a speed of approximately two steps per second. Stimuli were based on motion capture data as previously described The biological motion stimuli  consisted of moving point-light displays without contours. Fifteen female and 15 male walkers were used; each was marked by 15 white dots at the joints, tracking movements at the joints of the limps displayed against a black background. This was created using Labview version 6 .The stimulus sequence was controlled by PresentationTM software (Continuous direct current EEG (DC-EEG) from 64 channels at a sampling rate of 500 Hz was recorded against a reference at Fpz using BrainVision MR-Plus amplifiers and Brain Vision Recorder software . An anti-aliasing low-pass filter with 250 Hz high cut-off was applied online. Sixty-four sintered Ag–AgCl electrodes (impedances<10 kΩ) were fixed by equidistant electrode caps . Vertical and horizontal electro-oculograms (VEOG and HEOG) were recorded from electrodes placed 1 cm above and below the left eye and lateral to the outer canthi.EEG-data was analysed using the Brain Vision-Analyzer 2 . Offline, raw EEG was high-pass filtered to 40 Hz (high cut-off) and recordings were transformed to an average reference. Continuous recordings were segmented into epochs of 3.5 s (starting 500 ms before stimulus onset until 3 s after onset). The first 500 ms of this epoch served as baseline. Only trials with correct responses were included in the analysis. The recordings were corrected automatically for eye movements and blinks by the algorithm of Gratton and Coles . Because no slow DC potentials were observable, no linear regressions to eliminate such trends were required. Artefacts were rejected automatically if the signal amplitude exceeded 150 µV . This step was controlled by visual inspection, and remaining artefacts were removed by an experienced EEG technician who was blind to the study hypotheses. Electrodes with poor impedance or too many artefacts  were interpolated by nearest neighbours. The number of available artefact-free trials was compared between both groups. There were no differences between both typically developing controls (82.1±7.1) and ADHD (81.6±7.8) in the biological motion condition (t(40) = 0.23; p = 0.82), nor in the scrambled condition  = 0.68; p = 0.5).Scalp regions and time windows of interest were defined according to previous studies and visual inspection of group grand averages of our data In some studies, a second negative component was observed In order to further elucidate the cortical areas implicated in biological motion processing, we performed a spatio-temporal dipole source analysis using BESA research 5.1.8, Brain Electrical Source Analysis  according to Krakowski et al. (2011). First, we calculated the difference waveforms between the human motion and the scrambled stimuli in order to eliminate basic motion from biological motion processing.et al., 2011). In contrast to Krakowski et al., the activation on our data peaked approximately 50–100 ms later. Therefore, we fitted two bilaterally symmetrical dipoles on the time interval 450–550 ms on the grand average of typically developing children, because of the better signal-to-noise ratio in grand averages compared to individual subject averages. Later time windows were not chosen, so as to minimize an overlap between the specific human motion activation and the P400+. Residual variance and dipole energy were minimized. Because the introduction of further dipoles did not further improve the model, the two-dipole solution was maintained.In typically developing children, the difference waveforms indicated a stable topography with an occipito-temporal negativity and a central/centro-parietal positivity see  in good Subjects with ADHD showed a more diffuse surface topography than typically developing subjects and lower amplitudes (see results section). Because of these low amplitudes, no reliable explanation of the surface topography was possible in the ADHD, as there were very high residual variances. Thus, no different source model was used for the ADHD group, but the source model fitted on typically developing children was applied to each individual subject. The fit procedure of the source model on the group grand average of typically developing children helped to minimize error variance that might distort dipole localization and/or orientation.The dipole activations during the window of 450–550 ms after stimulus onset were exported for further statistical analysis in order to quantify the difference in the strength of the dipolar pattern reflected by the source model between groups.In an additional, complementary approach to describe the more diffuse brain activity in ADHD, we also compared the amount of variance of the surface potential explained by the dipole model between groups. Accordingly, dipole orientations were refited for each individual subject's average and the explained variance  was exported for statistical analysis.Statistical tests and correlation-coefficients were chosen according to the distribution of the dependent variables of interests. To test for group differences in percentiles of Raven matrices and handedness non-parametrical tests (Mann-Witney-U tests) were performed. Age, SCQ score and ADHD symptoms were compared between typically developing controls and ADHD by using a t-test. Group differences in number of correct responses and reaction times were tested with an ANCOVA with the within-factor MOTION TYPE (‘human-’/’scrambled motion’), between-factor GROUP, and age as covariate. Differences in ERP amplitudes and latencies were analysed by repeated-measures ANCOVAs with the within-factors MOTION TYPE and HEMISPHERE , the between-subjects factor GROUP, and AGE as a covariate. The P400+ was analysed by a repeated-measures ANCOVA with the within-factor MOTION TYPE, between-subjects factor GROUP, and AGE as covariate. Dipole moments in the right and left hemisphere obtained by source analysis were analysed by ANCOVAs with the factor GROUP, and AGE as covariate. The amount of variance of the surface potential explained by the source model was compared between groups by an ANCOVA with the between-subject factor GROUP, and AGE as a covariate.Additionally we tested for differences between ADHD subjects with (N = 9) and without (N = 12) comorbid disruptive behaviour disorder in the ERP components or dipoles using t-tests. Furthermore, to exclude potential confounding effects of disruptive behaviour disorders, analysis between ADHD and typically developing controls were repeated but without the nine subjects with ODD or CD. In both approaches we focused at components, which differed between ADHD and typically developing controls in the ANCOVA models .We further calculated exploratory Pearson correlations between SCQ score  and ADHD-symptoms (only ADHD subjects) with the N200 amplitude (averaged for both hemispheres and conditions) and the dipole activation (averaged for both hemispheres).Significance levels were set to p = 0.05. The statistical analysis was performed using SPSS version 20.Descriptive data regarding age, handedness, IQ and SCQ score are summarized in Similar to previous studies In the human motion condition, typically developing controls showed 94.2% correct responses and subjects with ADHD showed 94.8%. In the scrambled motion condition, typically developing controls showed 92.9% and ADHD 91.4% correct responses. There were no significant differences in the number of correct responses between the two groups <1; p = 0.77) nor between both conditions <1; p = 0.34; see Regarding the P100 component, there was a negative association between age and P100 amplitude  = 10.2; p = 0.003; r = −0.46) as well as a trend for an interaction of HEMISPHERE*GROUP  = 3.3; p = 0.08). In typically developing controls, the amplitudes were slightly higher in the right hemisphere at O2, while in ADHD amplitudes were equally high at O1 and O2 . There wIn respect to the N200 an effect of HEMISPHERE  = 6.0; p = 0.02) with higher amplitudes in the right hemisphere compared to the left, and an effect for GROUP  = 6.5; p = 0.01) with reduced amplitudes bilaterally in ADHD were observed. Furthermore, a trend for the interaction HEMISPHERE*AGE  = 4.0; p = 0.05) was also observable, however, neither the correlation between age and averaged activation in the right hemisphere  nor averaged activation in the left hemisphere  reached significance. There were also no significant effects regarding the latency of the N200 .There were no significant differences in N200 amplitude (t(19) = 1.0; p = 0.34) between ADHD subjects with or without comorbid CD or ODD  was found .The equivalent dipoles were located at the occipito-temporal junction, between the pSTS and MT+/V5. Both dipoles were located at a certain depth in the brain, indicating that they reflected the centre of gravity of larger active brain areas. Occipito-temporal negativity and mid-central/mid-centro-parietal positivity were explained by the same sources using volume conduction. The time-course of activity is shown in There was a significant effect for GROUP  = 4.3; p = 0.04) and AGE  = 7.3; p = 0.01) with reduced activation in ADHD. The age effect mainly resulted from a negative correlation between age and the left dipole activation  = 7.2; p = 0.01; r = −0.40; see The two-dipole model explained 88.5% of variance of the difference waves elicited by human vs. scrambled motion in typically developing controls, and 82.6% in ADHD. Also when dipole orientations were refit to single subject averages, the dipole model explained significantly less variance in the ADHD group (54.8±17) compared to typically developing controls  = 5.3; p = 0.03).In addition, there were no differences between ADHD with or without comorbid CD or ODD in the left dipole activation (t(19) = 0.9; p = 0.38), nor in the explained variance of both dipoles (t(19) = 0.6; p = 0.53; see We found no significant correlation between N200 amplitude and attention deficits  or hyperactive/impulsive symptoms . There were trends (without correcting for multiple comparisons) for the correlation between the dipole activation and attention deficits  and hyperactive/impulsive symptoms .There were no correlations between the SCQ scores and neurophysiological parameters of human motion processing in typically developing controls  or in ADHD subjects .The main aim of this study was to clarify if processing of biological motion is disturbed in children and adolescents with ADHD. We observed reduced activity in motion sensitive components (N200), to both biological and scrambeld motion, as well as a reduced activation in dipole sources which is specific for biological motion processing. Differences on the behavioural level were not found. In the following paragraphs, the results will be discussed in detail.After about 100 ms a first positive deflection at occipital sites was observed. In typically developing controls, the amplitudes were slightly higher in the right hemisphere, while in ADHD amplitudes were equal in both hemispheres. Indeed, biological motion has been found to be processed predominantly in the right visual cortex After 200 ms a negative deflection was observed at electrodes P10 and P9 – likely reflecting activity in MT+/V5 After 400 ms a slow and late positive deflection at parieto-central electrodes became evident. While earlier components reflect automatic processes, the P400+ is supposed to be a marker of a top-down, active cognitive process involved in decoding the stimulus content We analyzed the difference waves between both conditions (‘human motion’–‘scrambled motion’) after 400 ms stimulus-onset by dipole source analysis. By comparing this difference, effects of non-biological motion processing are expected to be removed with the aim to investigate brain systems specifically involved in the detection of biological motion Specific activations to biological motion were previously described in fMRI studies in the posterior temporal cortex, including regions also important for the processing and perception of social stimuli The ADHD group showed a significantly reduced activity after 450 ms post stimulus-onset in both dipoles. In addition, the variance of the cortex activation explained by both dipoles was lower in the ADHD group compared to typically developing controls. Interestingly, reduced or atypical activation in temporal regions in response to biological motion was observed in ASD before Despite the observed differences on the neurological level we found no observable differences on the behavioural level – both groups were equally capable in identifying the stimuli. Such dissociations between neuronal activation and behavioural performance in biological motion perception were also found in ASD. For example in one fMRI study Another nice approach was adopted in one recent study We found no further differences between ADHD subjects with and without a comorbid disruptive behaviour disorder in the N200 amplitude and dipole activation. Furthermore, even after the exclusion of nine subjects with CD/ODD, the effects regarding the N200 amplitude and dipole activation remained robust. This indicates a stronger relation between ADHD and social processing deficits in this population than with comorbid disruptive behaviour disorders. Alternatively the negative results might be due to small sub-sample size. In our study, only nine ADHD subjects were diagnosed with a comorbid disruptive disorder. Larger sample sizes are needed to test for differences between these sub-populations and exclude confounding effects of comorbid disruptive behaviour disorders.In sum, for the first time we describe changes in biological motion processing in ADHD which starts 200 ms after stimuli onset in motion sensitive areas and continues at higher, more specialized processing stages. Thus our results suggest some alterations also in higher-ordered networks responsible for biological motion processing in ADHD. However, there are further open issues: first, we only included male participants, thus these results cannot be generalized to female populations. Secondly, the study sample size was still modest and the results need to be replicated in larger and also more heterogeneous samples (e.g. different comorbid disorders). Third, it needs to be addressed in future studies to which extend these disruptions are influenced by simple attention allocation deficits – and might therefore normalise in case of medication. Finally, it is important to point out, that as long as no deficits in the perception of biological motion stimuli are found it will remain unresolved whether the reduction of brain activity in ADHD really indicates a deficit in biological motion processing. Further studies need to address this question.Table S1Mean amplitudes (µV) and latencies (ms) for all ERPs and mean dipole activation (nAm) separate for participants with ADHD and typically developing controls (TC).(DOC)Click here for additional data file.Table S2Differences between ADHD with CD/ODD (N = 9) and without (N = 12) regarding the N200 and dipole activation.(DOCX)Click here for additional data file.


Attention-Deficit/Hyperactivity Disorder (ADHD) affects approximately 5% of children worldwide and results in significant impairments in daily functioning. Few community-ascertained samples of children with ADHD have been studied prospectively to identify factors associated with differential outcomes. The Children’s Attention Project is the first such study in Australia, examining the mental health, social, academic and quality of life outcomes for children with diagnostically-confirmed ADHD compared to non-ADHD controls. The study aims to map the course of ADHD symptoms over time and to identify risk and protective factors associated with differential outcomes.The sample for this prospective longitudinal study is being recruited across 43 socio-economically diverse primary schools across Melbourne, Australia. All children in Grade 1, the second year of formal schooling (6–8 years), are screened for ADHD symptoms using independent parent and teacher reports on the Conners’ 3 ADHD index (~N = 5260). Children screening positive for ADHD by both parent and teacher report, and a matched sample  screening negative, are invited to participate in the longitudinal study. At baseline this involves parent completion of the NIMH Diagnostic Interview Schedule for Children IV (DISC-IV) to confirm likely ADHD diagnostic status and identify other mental health difficulties, direct child assessments  and questionnaires for parents and teachers assessing outcomes, as well as a broad range of risk and protective factors . Families will be initially followed up for 3 years.This study is the first Australian longitudinal study of children with ADHD and one of the first community-based longitudinal studies of diagnostically confirmed children with ADHD. The study’s examination of a broad range of risk and protective factors and ADHD-related outcomes has the potential to inform novel strategies for intervention and prevention. Attention-Deficit/Hyperactivity Disorder (ADHD) is the most common neurodevelopmental disorder, affecting approximately 5% of children worldwide . It is nADHD is characterised by deficits in executive functioning   and motiThere is a substantial literature addressing the aetiology and treatment of ADHD, with studies examining the course of ADHD over time mostly focussed on children selected through clinical settings -13. ThesWhile previous research provides valuable information about the outcomes for clinic-referred children with ADHD, it tells us little about children not accessing clinical services ,22. AustCommunity-based longitudinal designs have the potential to address knowledge gaps regarding the course of ADHD and provide findings that are more applicable to the general population of children with ADHD. However, most existing community-based studies are limited in their assessment of ADHD status. For example, one common approach is to ask parents whether their child has ever received a diagnosis of ADHD. This approach is likely to contain measurement error arising from parents who indicate ‘yes’ based, for example, on professional opinion rather than formal diagnostic assessment. Furthermore, this approach does not assess whether symptoms are current, and as with the studies of clinical samples of children with ADHD, this approach will only include children who do get referred and assessed for ADHD. The second common approach is to define ADHD status using parent and/or teacher-reported ADHD symptom checklists ,25. The To date, there have been few community-based longitudinal studies of children with diagnostically confirmed ADHD and controls -29. BarbFurther research in this area would benefit from prospective community-based designs that include both direct assessment of children’s functioning and multi-source  data on potential risk and protective factors for poor long-term outcomes. Regular follow-up assessments are required to enable examination of subtle changes in children’s symptoms. This is important in understanding the developmental trajectories for children with ADHD and the predictors of poor versus better outcomes in this population.Socio-ecological models that highlight the multitude of individual and contextual factors shaping children’s health and development provide a useful framework for conceptualising the factors likely to influence the persistence of ADHD and associated outcomes. The socio-ecological model of children’s health that underpins the selection of constructs to be measured in our study is shown in Figure Macro-environmental  and distal social environments  are important factors influencing children’s developmental trajectories, but little attention has been given to these factors within existing longitudinal studies examining outcomes for children with diagnostically-confirmed ADHD. Key proximal influences are also critical and may include parent–child interactions, parental mental health, and children’s relationships with their teachers and peers. Longitudinal studies of children with ADHD have identified that family context -32, chilThe present study will establish a community-based cohort of 200 6–8 year old children with diagnostically-confirmed ADHD and matched non-ADHD controls. The cohort will be tracked across the early years of primary (elementary) school to determine the mental health, academic and social outcomes for children with and without ADHD and to identify the factors associated with differential outcomes within the ADHD group. Within a community-based sample, the primary aims of this study are to:1. measure ADHD symptoms over the early school years;2. evaluate the extent to which children with ADHD have elevated risks for poor academic, mental health, quality of life, and social outcomes compared to non-ADHD controls;3. evaluate the extent to which parents of children with ADHD have elevated risks for poor mental health and quality of life compared to non-ADHD controls; and4. assess the influence of ADHD subtype and symptom severity, comorbidities, and other child, family, socioeconomic and school factors on mental health, academic, quality of life, and social outcomes.The Children’s Attention Project is a longitudinal cohort study conducted in accordance with STROBE guidelines . The proParticipants are Grade 1  children and their parents and teachers from 43 government primary schools in Melbourne, Victoria. Grade 1 was chosen as the recruitment point, as this is a developmental stage at which children with ADHD often begin to manifest impairments related to the functional impact of their symptoms. Earlier sampling would result in missed cases. Although later sampling would increase the yield of cases, opportunities to reliably record and then track key early influences on developmental trajectories would be lost.Exclusion criteria include: intellectual disability; serious medical conditions; genetic disorders; moderate-severe sensory impairment; neurological problems; and parents with insufficient English to complete interviews/questionnaires.Schools were recruited via the state department responsible for government schools (Victorian Government Department of Education and Early Child Dev). Five school networks in two Melbourne metropolitan regions (Eastern and Western) were approached. The networks were selected to provide access to schools from diverse socioeconomic communities as recorded in administrative departmental data using the Index of Community Socioeconomic Advantage (ICSEA), which has a mean of 1000 and a standard deviation of 100 . FollowiWith parental consent, all Grade 1 children in 2011 and 2012 in participating schools are screened for ADHD symptoms using a two-step process. The first step involves parent completion of two-page screener containing: the 10-item Conners 3 ADHD Index ; a questboth parent and teacher Conners 3 ADHD Indices are equal to or above a percentile cut-point for age and gender (high score = high indicating more symptoms). The 75th percentile is used for boys and the 80th percentile for girls. These cut-points were selected based on our pilot study data, which demonstrated good correspondence with DSM-IV ADHD criteria on the NIMH Diagnostic Interview Schedule for Children IV (DISC-IV) [Children are considered to have screened positive for ADHD if scores on DISC-IV) . As crosboth parent and teacher Conners 3 ADHD Indices are less than the 75th percentile for age for boys and less than the 80th percentile for age for girls, the child has no parent-reported diagnosis of ADHD, the child does not meet exclusion criteria and the parent has provided consent for further follow-up. For each positively screened child, a negatively screened child is randomly selected from the pool of negatively screened children, matched on gender and school.Children are considered to have screened negative for ADHD and eligible for inclusion if scores on The parents of all children screening positive and the randomly matched children screening negative, are telephoned to invite them into the longitudinal study. If consent is provided, all children have their ADHD status confirmed by the DISC-IV interview conducted with parents at home or at the child’s school according to preference. Direct child assessments of cognitive, academic, language, and executive functioning, plus height and weight measurements, are conducted at the child’s school. Interviews and direct assessments are completed by field staff who have at least a four-year undergraduate degree in psychology, are comprehensively trained in all procedures, and are blind to child screening status. To ensure blinding, no staff member undertakes both parent and child assessments for any given family.With regard to medication, children are assessed in their usual classroom condition. For example, if children usually take medication for ADHD or any other mental health condition, they are not asked to cease medication for the assessment. Parents who report at the recruitment phone call that their child is taking medication to assist with emotions, learning and/or behaviour, are called the day after the child assessment to assess whether the child took any medication on the day of the assessment, and to collect additional data on the type, dose, and frequency of medication use, perceived benefits and side effects of medication.Parents and teachers are asked to complete detailed baseline questionnaires covering a range of predictor and outcomes variables. Teachers are not informed of the screening status and diagnostic status of participating children. Parents and teachers who do not return questionnaires within two weeks, receive a reminder letter, SMS reminder and telephone call at regular intervals.We also seek optional parent consent to retrieve/link data from complimentary data sources about health service use , pre-school commencement health and well-being , and school functioning , and academic functioning .Three groups of children will be followed up over time:1) ADHD group: children who screen positive for ADHD and meet diagnostic criteria for ADHD on the DISC-IV;2) At risk group: children who screen positive for ADHD but do not meet criteria for ADHD on the DISC-IV or children who screen negative for ADHD but meet criteria for ADHD on the DISC-IV; and3) Non-ADHD controls: children who screen negative for ADHD and do not meet criteria for ADHD on the DISC-IV.At each follow-up, parents and teachers are asked to complete detailed questionnaires with the 36 month follow-up enabling tracking of outcomes through to the cusp of puberty (age 9–11 years), a critical developmental transition point. We will use the same reminder processes as used at baseline for our parent and teacher follow-up questionnaires.Measures are summarised in Table In order to detect a 0.3 SD clinically meaningful difference between the ADHD group and controls on each of our continuous primary outcomes, with 80% power and at a significance level of 0.05, we need to recruit 200 children in the ADHD group and at least 200 children in the control group. Based on data from the pilot study, we need to distribute ADHD screening questionnaires to approximately 5,300 parents in order to have data on 175 children with ADHD at follow-up. This assumes an initial 70% response rate to the combined parent/teacher screening questionnaires of whom 10% screen positive for ADHD, and an 80% consent rate to the diagnostic confirmation and baseline assessment, of whom 70% of children screening positive then meet criteria for ADHD on the DISC-IV. We have allowed for 15% attrition over the three subsequent follow-ups.Children whose screening status differs from their subsequent diagnostic status will be followed as an ‘at risk’ sample. Based on data from our pilot study, we anticipate that approximately 30% of the sample who screen positive for ADHD and 3% of those who screen negative, will fall into the at risk group. Based on the sample size in the present study this will equate to approximately 98 children at baseline.We will compare the proportions of children meeting diagnostic criteria for other DSM-IV mental health diagnoses (DISC-IV) between the ADHD and control groups using the Chi-squared test for each of the diagnostic categories and the Mann Whitney test to compare symptom counts . We will compare mean differences in parent and teacher reported total behavioural and emotional problems, social functioning, parent-reported child quality of life, measured academic achievement, and teacher-reported classroom performance between the two groups using t tests . Furthermore, we will compare mean differences in parents’ levels of depression and family quality of life between the two groups using t tests.We will then conduct regression analyses (linear regression for continuous data and logistic regression for binary data) for each outcome, adjusted for potential confounders identified a priori . We will also repeat all of the above to explore differences between ADHD, at risk, and control groups.We will conduct multivariable regression analyses (linear or logistic as appropriate) within the ADHD, at risk and control groups separately to determine the predictors of poorer (or better) functioning in each of our primary outcome domains . Predictors will include (depending on the outcome domain being examined): ADHD subtype, ADHD symptom severity, comorbidities, demographics, cognitive functioning, executive functioning, communication skills, parenting and couple factors, parental mental health, family QoL, teacher-child relationships, teacher/school characteristics and treatment factors. Tests of interaction between ADHD status and these predictors will be used to identify those that uniquely predict poorer or better outcomes within the ADHD group.ADHD is a highly prevalent condition which results in significant impairments across multiple domains. The majority of the research examining outcomes for children with ADHD has been based on clinical samples of children with ADHD. This study will be one of the first detailed community-based longitudinal studies worldwide focusing not only on the outcomes for children with ADHD, but the predictors of outcomes for this group. By identifying the factors influencing outcomes, this study has the potential to inform novel strategies for intervention and prevention, addressing critical questions of when to intervene, with whom, and which modifiable risk and protective factors should be targeted. We are currently planning a number of additions to this project including a neuroimaging and genetic arm. By improving knowledge about the natural course of ADHD, findings will be relevant to patients and families, mental health, education, allied health and medical professionals, and policy makers. The study will also establish the foundations for subsequent data collection into late primary school, and across the transitions to adolescence and adulthood.th Edition; WISC IV: Wechsler Intelligence Scale for Children 4th Edition; STRS: Student-Teacher Relationship Scale.ADHD: Attention-Deficit/Hyperactivity Disorder; DISC-IV: NIMH Diagnostic Interview Schedule for Children IV; ICSEA: Index of Community Socioeconomic Advantage; LSAC: Longitudinal Study of Australian Children; SSIS: Social Skills Improvement System; PedsQL: Pediatric Quality of Life Inventory 4.0; WRAT 4: Wide Range Achievement Test 4; K 6: Kessler 6; CHQ: Child Health Questionnaire; SCQ: Social Communication Questionnaire; WASI: Wechsler Abbreviated Scales of Intelligence; TEA-Ch: Test of Everyday Attention for Children; Beery VMI: Beery-Buktenica Developmental Test of Visual-Motor Integration, 6PH’s institution has received payment from Eli Lilly and Janssen for consultancies; Eli Lilly, Janssen, Novartis and Shire for advisory boards; Eli Lilly, Janssen, Pfizer and Shire for speaker’s bureau; Eli Lilly and Celltech for the conduct of clinical trials. ES, DE, ES VA, BJ, OU and JN have no conflicts to declare.ES, DE, VA, PH, BJ, OU, and JN contributed to the overall design and conception of the study and assisted with the writing of the grant application and revised this manuscript. ES, EJS and JN drafted this manuscript. EJS and ES contributed to study implementation. OU contributed to the statistical design of the study. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/13/18/prepub