B4GALT2, SLC6A9 TLE1, ANK3, TRIO, TAF1, and SYNE1) were confirmed to be significantly DE in our study. Integrated miRNA and mRNA expression data identified critical key hubs involved in ADHD. Finally, the GO and pathway enrichment analyses of all DE genes showed their deep involvement in immune functions, reinforcing the hypothesis that an immune imbalance might contribute to the ADHD etiology. Despite the relatively small sample size, in this study we were able to build a complex miRNA-target interaction network in children with ADHD that might help in deciphering the disease pathogenesis. Validation in larger samples should be performed in order to possibly suggest novel therapeutic strategies for treating this complex disease.Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental disorder, whose etiology and pathogenesis are still largely unknown. In order to uncover novel regulatory networks and molecular pathways possibly related to ADHD, we performed an integrated miRNA and mRNA expression profiling analysis in peripheral blood samples of children with ADHD and age-matched typically developing (TD) children. The expression levels of 13 miRNAs were evaluated with microfluidic qPCR, and differentially expressed (DE) mRNAs were detected on an Illumina HiSeq 2500 genome analyzer. The miRNA targetome was identified using an integrated approach of validated and predicted interaction data extracted from seven different bioinformatic tools. Gene Ontology (GO) and pathway enrichment analyses were carried out. Results showed that six miRNAs  and 560 genes were significantly DE in children with ADHD compared to TD subjects. After correction for multiple testing, only three miRNAs  remained significant. Genes known to be associated with ADHD (e.g.,  Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental disorder characterized by inappropriate and impairing inattention, impulsivity, and hyperactivity . The disADHD is considered a complex disorder caused by environmental , epigeneTo date, many critical features still need to be clarified about ADHD: the etiology and pathogenesis of the disease are still largely unknown, and its diagnosis continues to be heatedly debated, as it is based on the observation of behavioral signs and verbal reports, rather than on measurable biological parameters; moreover, personalized and efficient therapeutic approaches are missing. Given the clinical heterogeneity of ADHD and its high comorbidity with other psychopathological disorders , there iMicro-ribonucleic acids (miRNAs), highly evolutionarily conserved endogenous small noncoding RNAs, have been investigated as biomarkers for the diagnosis of ADHD, as well as for monitoring its progression and response to treatment ,16,17. MThe aim of this study was to identify critical key hubs and aberrant miRNA-based regulatory networks in order to provide deeper insight into the genetic mechanisms underlying this multifactorial and complex disease, thus helping to clarify the gray areas of ADHD. To this purpose, we investigated the expression profile of several circulating miRNAs in children with ADHD employing a miRNA panel tested for the study of other neurological diseases . Using aThe study recruited children diagnosed with ADHD, according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria, among those diagnosed, followed-up, and consecutively presenting at the Child Neuropsychiatric Unit, University of Bari, Italy. ADHD patients were less than 18 years old and they were not previously exposed or under concomitant psychotropic drugs. Children with a history of comorbid major psychiatric disorders, such as autism spectrum disorder, bipolar disorders, major depression, obsessive-compulsive disorders, psychotic disorders, epilepsy, or severe head injury, were excluded. Age-matched typically developing (TD) children were recruited from the same geographic area; they did not show clinical signs or instrumental evidence for any of the aforementioned major psychiatric disorders.The study was approved by the Ethics Committee of Azienda Ospedaliera Policlinico, University of Bari. Written informed consent (according to the Declaration of Helsinki) was obtained from the parents or legal tutors of the children enrolled in the investigation.Peripheral blood samples were collected from children with ADHD and controls and stored at –20 °C in 3 ml PAXgene Blood RNA Tubes . Total RNA was isolated using the PAXgene Blood RNA Kit . RNA quantity and quality were measured using Nanodrop ND-1000 spectrophotometer  and RNA 6000 Pico chip on Bioanalyzer 2100 , respectively. Samples with RNA integrity number (RIN) scores higher than 7 and with A260/A280 values in the 1.8–2.2 range were processed for further analyses. TaqMan Advanced miRNA Cards  were employed for miRNA quantitative analysis. Approximately 8 ng of total RNA/sample was reverse transcribed using a TaqMan Advanced miRNA cDNA synthesis kit . The ends of each mature miRNA were extended with 5′-end ligation of an adaptor sequence and 3′ poly-A tailing, and recognized by universal RT primers . The obtained cDNA was amplified using the Universal miR-Amp Primers , diluted, and served as a template for microfluidic qPCR analysis with TaqMan Advanced miRNA Cards . Briefly, 25 µL of preamplified product was mixed with 50 µL of TaqMan Fast Advanced Master Mix  and dispensed into each port of the TaqMan Advanced miRNA Card. The reaction was performed on ABI PrismVR 7900HT sequence detection system  according to the parameters reported in a previous study . In deta−ΔΔCt method, detailed in [t-test. The obtained p-value was corrected for multiple comparisons using Benjamini–Hochberg’s method. A p-value <0.05 was considered statistically significant.Raw Ct-values were calculated using Expression Suite™ software v1.1 . The cycle number at which the reaction crossed an arbitrarily placed threshold (Ct) was determined for each miRNA. We used Ct = 40 as a cut-off. The relative expression levels of each miRNA, normalized to miR-191-5p and miR-93-5p (the most suitable endogenous reference miRNAs resulting from NormFinder and GeNorm tools), were calculated according to the 2ailed in . We perfp-value < 0.05; the Youden cut-off method was used to determine cut-off values. The associated p-values and area under ROC (AUC) were calculated for each miRNA. The miRNA’s ability to discriminate the compared groups (ADHD versus TD) was determined using receiver-operating characteristic (ROC) analysis, calculated with the easyROC web-tool . StatistmRNA libraries were prepared using the TruSeq Stranded mRNA Sample Preparation kit (Illumina). Briefly, 1 µg of total RNA was used for poly-A mRNA selection with oligo-dT beads, followed by thermal mRNA fragmentation and reverse transcription (RT). The obtained cDNAs were 3′-end adenylated and ligated to Illumina paired-end sequencing adapters and subsequently amplified by 12 cycles of PCR. The libraries were fluorimetrically quantified and analyzed, pooled together to obtain equimolar concentrations into a multiplex sequencing pool, and sequenced to generate 2 bp × 100 bp paired-end reads (around 30 million reads/sample) using an Illumina HiSeq2500 platform. p-value was <0.05. RNA-Seq data of ADHD and TD samples were processed, according to an in-house developed bioinformatics pipeline based on the standard tools developed for NGS data elaboration: sequencing quality was assessed with FastQC ; mRNA reIn order to identify genes closely related to ADHD, the list of DE genes, obtained through the previous pipeline, was matched to public available ADHD-associated genetic factors by using ADHDgene Database . This daSeven publicly available tools of predicted and validated miRNA-target interactions were used: miRanda , DIANA-mIn order to reduce the probability of false positive results, only those miRNA/mRNA bindings that were confirmed by reporter gene assays in the previously mentioned databases, or computationally predicted by at least three algorithms, were finally selected.https://david.ncifcrf.gov/) tool and analyzed by one-tailed Fisher’s exact test followed by the Benjamini correction with a threshold p-value < 0.05. Functional and pathway enrichment analysis of identified DE genes were performed using the Database for Annotation, Visualization and Integrated Discovery . A gender-related discrepancy was registered, since the ADHD patients were all males compared to 14 males and 6 females in the TD group (p = 0.0316); to avoid any biases due to gender differences, all the comparisons were performed between the ADHD patients and both the 20 TD subjects and the 14 male TD. Since the results did not change (This study included 9 children with ADHD with a mean age of 9.78 (SD 2.63) and 20 TD subjects with a mean age of 8.83 (SD 3.26). No statistically significant difference was detected between the two groups by age  in children with ADHD using a miRNA panel tested for the study of other neurological diseases [p-value, adj. p-value < 0.05). Despite this result, given the small sample size and the functional significance of the over-threshold miRNAs, we enclosed in the analysis of the targeted mRNAs all the six miRNAs  (see discussion for comments).The comparison of miRNA expression levels within the study groups revealed six mature miRNAs significantly DE between ADHD and TD . After Bp-value = 2.84 × 10−3), miR-942-5p , let-7b-5p , miR-181a-5p , and miR-320a  and statistically significant downregulation of miR-148b-3p  in children with ADHD compared to TDs  versus the 14 TD males, and we confirmed that the same six miRNAs were significantly different between the two groups . p-value < 0.05). The six miRNAs provided values of the area under the curve (AUC) higher than 0.7, discriminating ADHD from TD  and miR-148b-3p . To discriminate between the compared groups, we performed receiver-operating characteristic (ROC) analysis using the easyROC tool . The ind from TD . The besp-value, adj. p-value) lower than 0.05.In order to obtain a list of mRNAs involved in ADHD susceptibility, differential expression analysis of RNA-Seq data between the two subgroups (ADHD vs. TD) was performed, using three criteria already tested in previous analyses ,23: (1) As result, 560 genes (200 upregulated and 360 downregulated) showed significant DE in children with ADHD compared to TD subjects .To identify the miRNA targetome, the 560 DE mRNAs and the 6 DE miRNAs were selected for gene target analysis, using an integrated approach of validated and predicted interaction data extracted from seven different bioinformatic tools. Using databases containing experimentally validated miRNA-target interactions (miRtarbase and DIANA-Tarbase), four miRNA-target pairs, validated by reporter gene assays, were selected. Furthermore, since the prediction of the target site of existing algorithms can be still characterized by low precision and poor sensitivity, we integrated the predictions of at least three out of five miRNA-target interaction tools  according to published guidelines . This extensive analysis was able to uncover 190 predicted miRNA-target pairs, including 131 target genes; three miRNA-target pairs  were found overlapping between the experimentally validated and computationally predicted miRNA-target interactions. KLHL14, ASH1L, MACF1, ZNF318, EPHA4, LAMC1, PPARGC1B, and SYNPO) were shared by three miRNAs.We therefore constructed the miRNA-based network  includinThe DE genes were matched to publicly available ADHD-associated genetic factors contained in the ADHDgene Database , and we http://david.abcc.ncifcrf.gov/). The three GO categories  were used to describe the gene product attributes , regulation of immune response , Fc-epsilon receptor signaling , antigen binding . The pathway analysis also revealed the enrichment of immune-related pathways, such as classical antibody-mediated complement activation , CD22-mediated B cell receptor regulation , Fc-epsilon receptor activation , B cell receptor signaling . Details of significantly implicated pathways and GO terms are reported in To explore the potentially involved physiological functions, all target genes were subjected to Gene Ontology (GO) and pathway enrichment analysis using the DAVID 6.8 Functional Annotation Tool . In fact, as in our approach, most of the studies investigated preselected miRNAs involved in other neurological disorders and/or in neurobiology mechanisms based on literature reviews or miRNA databases ,17,21; tIn our investigation, starting from a panel of miRNAs dysregulated in diseases like Pediatric Multiple Sclerosis (PedMS) , we idenUsing for the first time a HT-NGS approach to profile mRNA expression in ADHD, we also obtained a plethora of DE genes compared to TDs. In the resulting miRNA-targets network, the targets with the largest degree values (in terms of number of incoming and outcoming edges) were KLHL14, ASH1L, MACF1, ZNF318, EPHA4, LAMC1, and PPARGC1B, suggesting that they might be critical elements in the ADHD physiopathology. Several of these downregulated targets have been previously associated to ADHD, such as TLE1 , ANK3 55,56, TRIRecently, a genome-wide analysis identified the first significant genetic variants associated with ADHD in 12 independent loci . The mosIn our analysis, many other identified miR-652-3p targets were related to mechanisms possibly associated with the ADHD pathophysiology. Among them, HIVEP2 has been reported in patients characterized by developmental delay and intellectual disability , and CHDFinally, the GO and pathway enrichment analysis of all the DE genes in our study reported their prevalent involvement in immune functions, such as complement activation, regulation of immune response, B cell receptor signaling, innate immunity, complement cascade, and adaptive immunity. Although from a limited number of cases, these results support the hypothesis that an immune imbalance may contribute to ADHD etiology, possibly requiring a predisposing genetic background , or it mIn conclusion, in this study we were able to draw a genetic profile of juvenile ADHD that may help in deciphering the disease’s pathogenesis. In spite of several already mentioned limitations, we believe in fact that our findings might provide preliminary but solid suggestions about some molecular networks that may be evoked during the occurrence of the disease. A larger cohort study and additional experimental approaches  need to be performed to confirm these preliminary results.


Adult attention-deficit hyperactivity disorder (ADHD) is a chronic, costly and debilitating disorder. In South Africa (SA), access to funding for care and treatment of ADHD is limited, and research is lacking.This study aimed to establish the current situation with regard to the psychiatric management of and funding for treatment of adult ADHD in the private sector in SA.A diagnostically refined retrospective claims database analysis was conducted. We examined the prevalence, costs and funding profile of claims over a 2-year period for adult beneficiaries with possible ADHD of a large medical administrator in SA.The prevalence of adult ADHD was lower than published international rates. The presence of adult ADHD increased the prevalence of comorbidity and doubled the health care costs of beneficiaries. Contrary to public belief, comorbidities (including their medicine costs) rather than psychiatric services or medicines were the main cost drivers.The current private health insurance funding model for ADHD limits access to funding. This affects early diagnosis and optimal treatment, thereby escalating long-term costs. Improved outcomes are possible if patients suffering from ADHD receive timely and accurate diagnosis, and receive chronic and comprehensive care. Balanced regulation is proposed to minimise the risk to both medical schemes and patients. A collaborative approach between stakeholders is needed to develop an alternative cost-effective funding model to improve access to treatment and quality of life for adults with ADHD in SA. Attention-deficit hyperactivity disorder (ADHD) has received increased scientific, clinical and public attention over the past few decades. ADHD is the most common psychiatric disorder in children, affecting 2.0% to 16.0% of the school-age population.5ADHD is a costly, chronic disorder, with significant impact on the quality of life (QOL) of patients and their families. The burden of disease (BOD) is significant, with the disability-adjusted life years (DALYs) calculated as 424 per 100 000.N > 100 000), resource utilisation of individuals with ADHD and their family members was contrasted with a matched control sample of patients without ADHD. The direct costs of ADHD in terms of annual average expenditure per patient, outpatient costs, inpatient costs and prescription drug costs were two- to threefold the costs of matched controls.In an analysis of all medical, pharmaceutical and disability claims in an administrative database  are an integral part of management.13 Medication options are often limited in emerging markets, and in SA, psychiatrists and patients do not have access to the medicines available in established markets.Despite the known efficacy of treatment and the substantial costs of untreated ADHD, access to health care and treatment is not a given for many patients in emerging markets. This holds true for SA where research indicates poor identification and treatment of common mental disorders at primary health care level and limited access to specialist resources with a service delivery and treatment gap of up to 75%.The lifetime prevalence of ADHD in SA is unknown. Extrapolating the known international prevalence information to the South African context, the expected number of adults aged between 20 and 50 affected by ADHD would be between 771 264 (3%) and 1 285 439 (5%).In SA, funding for treatment for children with ADHD is private, either via medical schemes or via the state sector (limited). However, adults with ADHD have even less access to care. Some medical schemes that cover for childhood ADHD often do not provide benefits for the treatment of adult ADHD, and patients can often not afford private treatment in addition to their monthly contributions to these funds.Our study, the first in the field in SA, aimed to establish the current situation with regard to the psychiatric management of and funding for treatment of adult ADHD in the private sector as the basis for a proposal for a new funding model in order to improve access to treatment and QOL for adults with ADHD in emerging markets such as SA.A triangulated study was conducted consisting of a retrospective claims database analysis, a survey and in-depth interviews. In this article, we report on findings on the quantitative analysis of a retrospective claims database using medical data, pharmacy data and enrolment information as captured for the largest administrator of medical schemes in SA, representing 3 million beneficiaries .To be included, claims submitted to the medical scheme had to be:for adult beneficiaries (aged 18–60)who had one or more outpatient medical claims for ADHD as indicated by relevant ICD-10 codes between 1 July 2011 and 30 June 2013who had received scripts or claimed for methylphenidate (MPH) derivatives, atomoxetine or bupropion as indicated by relevant National Pharmaceutical Product Index (NAPPI) codes.In SA, only MPH derivatives and atomoxetine are registered for the treatment of ADHD. International guidelines also include, amongst others, buproprion – which is used off-label for the treatment of ADHD in SA – especially in adults who tolerate the other registered drugs poorly. Although other drugs such as clonidine, guanfacine, tricyclic antidepressants, modafanil and venlafaxine are mentioned in the guidelines as third-line treatment for ADHD, none of these are indicated for this specific use in SA and are therefore excluded from this study. Concerns about diversion of medication  and non-compliance are relevant, but impossible to ascertain. For the purposes of this study, it was assumed that patients who fill their scripts at the pharmacy are indeed compliant with the use thereof.Medical data, pharmacy data and enrolment information as captured for a large medical scheme administrator during the study period were analysed. Data were collected during September 2013 as all delayed claims were captured by then.Descriptive statistics and correlations were used to describe the patient profile, disease profile, practitioner profile, treatment profile and funding profile. All analyses were conducted with STATISTICA, Version 12 and Microsoft Office Excel 2013.Approval for the project was obtained from the University of Stellenbosch Business school ethics committee.Note: Results in the text are reported as mean ± sd.For the period under investigation, the total number of beneficiaries managed by the medical scheme administrator  in the age group under investigation was 1 390 654. To be included in the sample, claims had to be linked with either an applicable ICD or NAPPI code (see Inclusion criteria section). In this study, the total number of claims  for the 2-year period analysed totalled 1 740 751, originating from 15 934 beneficiaries. These claims totalled R812 916 038.20, amounting to 1.91% of the total value (R42 670 01 47 96) of claims.Duplicates  were merged for a total number of 15 203 who fulfilled the inclusion criteria . The mean age of beneficiaries in the study sample was 33.83 ± 12.64 years at the time the first claim for the beneficiary was captured. The mean number of claims per beneficiary was 115 ± 138, with a median of 138 claims.n = 620 574, 35.65%). This is to be expected as almost every other service contact (e.g. for comorbid conditions) will generate a script for medication. This also reflects the issuing of chronic medication or repeat scripts without another service contact. The second-most claims were generated by general practitioners (GPs)  – the first point of health care service contact for most patients and for most conditions. Private hospitals and pathologists followed closely with 224 496 (12.90%) and 205 921 (11.83%) claims, respectively. Psychiatrists’ claims contributed to only 2.7% of claims (n = 46 974), whereas psychiatric clinics generated 0.20% of claims (n = 3490). Other service providers each contributed to less than 0.3% of the total claims  were not linked to the relevant codes. This contributed to 94.08% of the total value of claims. This may be because of the claim being unrelated to ADHD, poor coding by service providers or the condition not being seen as the primary reason for the service contact (e.g. therefore not indicated during submission of claims or on scripts). Some of these claims might in fact still have been associated with the presence of ADHD.per se. Furthermore, 6257 (0.35%) claims were associated with NAPPI codes unrelated to ADHD. This contributed to 0.44% of the total value of claims. Medical practitioners are not consistent in documenting the F-codes on scripts. The majority of the codes linked to psychiatrists and GPs will be reflective of consultations linked to the diagnosis (primary or secondary diagnoses), whereas some of the claims may also be linked to medication dispensation. These indicate claims that originated because of comorbid conditions (i.e. the ADHD was not the focus of attention) or claims where ADHD received non-conventional treatment (e.g. medication which was used off-label).Of the 10 548 (0.61%) claims linked to ICD codes for ADHD, 44.73% were generated by pharmacies, followed by GPs (27.45%) and psychiatrists (12.12%). Although pharmacies submitted most claims linked to the diagnostic code, these reflect scripts issued according to the diagnostic codes as documented by other service providers (e.g. GPs and psychiatrists) and are not reflective of diagnosis n = 5172, 97.14%), whereas private hospitals dispensed 1.62% (n = 863) and GPs 0.98% (n = 472). Of these claims, 48 941 claims (2.81%) were linked to ICD codes unrelated to ADHD, contributing 4.91% to the total value of claims. This could be an indication of off-label use of ADHD medication (e.g. as cognitive enhancement in patients who do not have ADHD or as augmentation in conditions such as treatment resistant depression) or poor script habits of service providers (e.g. where the diagnostic codes are not indicated on the scripts).Of the 53 232 (3.06%) claims linked to NAPPI codes (i.e. medication dispensed) for ADHD, the majority were generated by pharmacies  claims included in the study sample amounting to 0.56% of the total value of claims in the study sample. These direct costs are attributable to medication costs , psychiatric services , GP consultation  and other.n = 409, 29.5%, 2.7% of the sample), non-organic sleep disorders , anxiety disorders , mood disorders  and substance-related disorders .In the study sample, 3106 (20.43%) of the 15 203 beneficiaries had non-psychiatric comorbid disorders (i.e. an ICD code other than psychiatric F-codes) see . Respiran = 763 892, 38.48%) of which 318 039 (16.00%) claims were for chronic medication and 355 313 (18.00%) claims for acute medication. Chronic medication refers to all medication obtainable by a repeat script – which would include atomoxetine and bupropion. Acute medication refers to all medications obtained on an acute script. MPH derivatives, although used as chronic medicine, were therefore included as acute medication because monthly scripts are needed for schedule 6 medication. When a patient’s chronic benefits are depleted, medication claims will also be submitted as acute medication claims.The single biggest contributor to the number of generated claims was medicines related  were for bupropion. The second most common drug prescribed for treatment of ADHD in the study sample was osmotic-controlled-release oral delivery system (OROS®) MPH , followed by IR MPH  and MPH LA . Atomoxetine lagged behind the other scripts .ADHD medication amounted to 5.47% of costs in the study sample. When examining the relevant NAPPI codes in the study sample, it is clear that the majority of claims  consisting of psychotherapeutic interventions were amongst the top 25 claim drivers, albeit the contribution, relative to other claim drivers, was relatively small.Although psychiatric consultations consisting of psychotherapeutic interventions were amongst the top 25 utilised services (in terms of number of claims generated), the contribution to cost, relative to other utilised services, was relatively small and not that impactful.n = 38), as well as the total cost of claims (TCC), the total benefits paid and the total cost to patients are summarised in The mean cost per claim (MCPC), the mean benefit per claim (MBPC) – which reflect the cost to scheme, and the mean cost to patient (MCTP) for each patient were calculated. The MCPC, the MBPC and the MCTP for each medical scheme option  and the administrative accuracy of pharmacies (NAPPI codes). Furthermore, the study sample was limited to restricted schemes only and may not be generalisable to open medical schemes.6As per Results section , the total value of claims generated for the patients in the study sample contributed 1.9% to the total value of claims for this age group. Considering our sample size, the presence of possible ADHD virtually doubled the costs  per claiming beneficiary. This is in accordance with the previous studies which found that direct costs in patients with ADHD amounted to up to three times the costs in matched controls.per se is, therefore, far less than the cost of treatment of comorbid conditions , as well as the use of ADHD medication for purposes other than ‘true’ ADHD . The total cost of ADHD medication cost in the study sample (5.47% of costs) is comparable with the findings of Birnbaum et al., who calculated medication costs for treatment of ADHD as 7% of all direct and indirect costs.As per Results section (Attention-deficit hyperactivity disorder), only 0.01% of the total value of all claims could be attributed to ‘true’ adult ADHD. The direct cost of treating ADHD per se, it appears that psychiatrists do emphasise the need for psychotherapy (see Results section: Treatment profile). Although psychiatric consultations consisting of psychotherapeutic interventions were amongst the top 25 utilised services (in terms of number of claims generated), the contribution to cost, relative to other utilised services, was relatively small and not that impactful.Although a comprehensive, multi-modal, management approach for adults with ADHD is recommended, the majority of services claimed for were medication related. Only 1% of the claims generated were for services rendered by psychologists. Claims for individual psychotherapy also amounted to 1% of claims. If we consider claims originating from psychiatrists for ADHD Comorbidity of disease and complications of psychiatric disorders are important cost drivers. Psychiatric comorbidity in our study was more prevalent than in the general psychiatric population for both anxiety disorders (13.1% vs. 8.1%) and mood disorders (13.8% vs. 4.5%).5 This may once again reflect access to care and diagnosis.Multiple comorbidities were also more prevalent in the study database sample than in the general population with 9.09% having had one additional psychiatric diagnosis (vs. 3.9%), while 10.95% had two or more psychiatric diagnoses (vs. 1.4%). However, comorbidity in this study was still lower than previous estimations in excess of 50% reported elsewhere.per se are not the main utilised service, nor the main cost driver in the study sample, despite the prevalence of psychiatric disorders. This may be indicative of a lack of access for patients to (proper) diagnosis and treatment with regard to ADHD. This is a concern with regard to the use of scheduled medication such as stimulants, where in the study sample it would be expected that patients at least had six-monthly (or at the least yearly) reviews should they suffer from ADHD. Within psychiatry, consultations remain the largest claim driver, but the biggest overall cost driver remains medication. It is difficult to determine whether warranted utilisation versus the unregulated or injudicious use of medications registered for the use in ADHD may be a significant cost driver. Areas where costs can potentially be contained would be medication, hospitalisations and special examinations.Psychiatric services Of the TCC, medical schemes paid 91.95% in benefits, whereas beneficiaries in the study sample were responsible for 8.05% of the costs. Because of the multiple medical schemes and options included in the researcher’s analysis, the study did not distinguish between specific services and medication paid from chronic benefits, savings accounts or as out of pocket (OOP) expense to beneficiaries. This is not in agreement with findings from a national survey amongst psychiatrists which indicated a heavier burden on patients with 35.98% of the costs of psychiatric consultations, 41.17% of medication for the treatment of ADHD, 62.20% of supportive and alternative therapies, occurring as OOP expenses.Adult ADHD is a chronic, costly and debilitating disorder. This study, the first in the field of adult ADHD in SA, aimed to establish the current situation in SA with regard to the psychiatric management of and funding for treatment of adult ADHD in the private sector.Lower than international prevalence rates may reflect a lack of awareness of the disorder, lack of access to diagnosis and treatment, and poor coding habits of health care practitioners. The cost drivers in this study were not psychiatric services or medication, but the presence of comorbid conditions, as well as use of medication for non-ADHD-related reasons.The current medical scheme funding model for ADHD limits access to diagnosis and optimal treatment, thereby escalating long-term direct, indirect and intangible costs and the BOD on the patients, their families and society. Improved outcomes are possible if patients suffering from ADHD receive a timely and accurate diagnosis and receive comprehensive care – which would include psychopharmacological interventions, behavioural interventions and support.It is proposed that schemes should recognise adult ADHD as a chronic disorder, which needs chronic treatment and, therefore, remunerate for services and medication from chronic benefits. To decrease the risk to medical schemes, balanced regulation is suggested. The bar should be raised in terms of receiving the diagnoses of adult ADHD, for example, through partnerships with psychiatrists or centres of excellence where comprehensive assessment is available to ensure that the threshold to obtaining the diagnosis is sensitive and specific. Guidelines should be established with regard to the diagnostic process, relevant special examinations, as well as recommended treatment regimes. Once the diagnosis is confirmed, a patient should have access to comprehensive treatment. Once stabilised, follow-up can take place at primary health care level, leading to further cost savings through use of relevant resources. A collaborative approach between stakeholders with public-private partnerships is crucial in this process of addressing research , education , information and communication  and improving practices . The aim is to break down the barriers with regard to a lack of knowledge and a lack of funding in order to provide access to care and treatment and to develop an alternative cost-effective funding model for the treatment of adult ADHD in SA.


ASD− and ASD+ groups did not differ by IQ or social functioning; however, ASD+ had significantly higher inattention and hyperactivity ratings. ICF (higher ratio indicates greater facilitation) in ASD+  was less than ASD−  or TDC  . No differences were found between groups for SICI or age corrected active/resting motor threshold (AMT/RMT). Across all ASD youth (ASD− and ASD+), ICF was inversely correlated with worse inattention  and ADHDRS-IV Inattention percentile  scores. ICF remains intact in ASD− but is impaired in ASD+. Lack of ICF is associated with inattention and executive function across ASD. Taken with the present findings, ADHD may have a distinct electrophysiological “signature” in ASD youth. ICF may constitute an emerging biomarker to study the physiology of ADHD in ASD, which may align with disease prognosis or treatment response.The neural correlates distinguishing youth with Autism Spectrum Disorder (ASD-) and ASD with co-occurring Attention Deficit Hyperactivity Disorder (ASD+) are poorly understood despite significant phenotypic and prognostic differences. Paired-pulse transcranial magnetic stimulation (TMS) measures, including intracortical facilitation (ICF), short interval cortical inhibition (SICI), and cortical silent period (CSP) were measured in an age matched cohort of youth with ASD- ( Youth with a co-diagnosis of ADHD and ASD (ASD+) have higher rates of hospitalization, medication treatment, and behavioral therapy than ASD alone (ASD−)2.Attention Deficit Hyperactivity Disorder (ADHD) is the most common co-occurring psychiatric condition diagnosed in Autism Spectrum Disorders (ASD) with an estimated 37–78% of individuals with ASD have symptoms of inattention, hyperactivity, and/or impulsivity and meet criteria for ADHD3. Despite the robust incidence of ADHD symptomatology in ASD, studies have found that ASD+ is under recognized and less frequently treated in ASD youth compared to otherwise typical children4. When the diagnosis is recognized, ASD+ tend to respond poorly to conventional treatments, such as stimulants and behavioral therapies, compared to ADHD present in otherwise typically developing children (TDC)7. The diagnosis and treatment of ASD+ is limited by a poor understanding of the neurobiology underlying the phenotypic differences in symptoms, severity, and treatment response from ASD-8.There remains an open question regarding the best methods to classify ADHD co-occurrence in ASD in the clinical setting9. Yet, since attentional problems are characteristic of both ADHD and ASD+, computerized testing demonstrates increased intrasubject variability in reaction time tasks in comparison to typical and ASD− groups10. Measures of social cognition, as expected, are more impaired in ASD+ than ADHD. Tye et al11. studied face processing through event related potentials in ASD−, ASD+, TDC, and ADHD. The group identified abnormalities in facial gaze processing and neuronal lateralization in the ASD groups (ASD+ and ASD−) compared to either ADHD or TDC. However, both ADHD affected groups (ASD+ and ADHD) groups had measured deficits in latency measures associated with early visual attention compared to ASD+ and TDC.One approach has been to identify quantitative, brain-based biomarkers of ADHD symptoms and compare these in children with ASD+ vs ADHD to identify objective measures which might be unique to ASD vs those which might overlap. For example, increased frontocentral theta activity (and to a lesser extent decreased beta activity) as measured by quantitative electroencephalography (EEG) is robustly related to inattention in ADHD, but has not been replicated in either ASD− or ASD+12. The consistency of these M1 SICI findings across multiple research groups was recently confirmed by meta-analysis13. However, it is unknown whether this relationship between reduced M1 SICI and ADHD is also present in ASD+.An alternative method to probe the underlying neural processes driving hyperactivity, inattention, and impulsivity symptoms in ASD+ is transcranial magnetic stimulation (TMS) interrogation of the motor cortex (M1) to probe cortical excitability. To date, this has uncovered various relationships with ADHD symptoms. Previous work by our laboratory using paired pulse TMS has demonstrated that reduced short interval cortical inhibition (SICI), is associated with both greater clinical symptom severity and more impaired motor development in children with ADHDThus, the goal of our study was to more clearly delineate neurophysiological differences in resting motor physiology, as a marker of cortical excitability, between ASD+ and ASD− to better understand what engenders phenotypic differences in hyperactivity, inattention, and other executive functioning deficits. Based on our previous work and prominent ADHD symptoms in ASD+, we expected to find decreased SICI in ASD+ compared to ASD− and control subjects. This would support the idea that there are overlapping neural processes related to hyperactivity, inattention, or impulsivity. Alternatively, widely divergent physiological findings in ASD+ from ASD− and ADHD may have implications for prognosis and treatment.14. Exclusion criteria included severe medical or neurological illness, substance use disorder, schizophrenia, bipolar disorder, or major depression. Subjects were excluded if they were taking medications affecting glutamate neurotransmission . Any subject prescribed a stimulant medication was asked to hold their dose for 24 h prior to the study date.Subjects with a known diagnosis of ASD between the ages of 8 and 21 years of age were recruited from a pediatric psychiatry clinic at a tertiary children’s medical center specializing in ASD and neuropsychological assessment. All ASD subjects had previously been diagnosed at the medical center following a standardized multidisciplinary evaluation which included the Autism Diagnostic Observation Schedule (ADOS)12 and event related potential studies in ASD− and ASD+11, sample sizes of approximately 20 per a group would have 80% power to detect a large effect size based on a two-tailed alpha criterion of 0.05. Though we had interest including an ADHD-only cohort from our previous studies, the ages and sex distribution were unevenly matched. All TMS data were generated using the same equipment, using the same experimental techniques. The IRB of Cincinnati Children’s Hospital Medical Center approved the conduct of this study . We obtained written informed consent from all participants and their parents.For TDC controls, a cohort of subjects with no psychiatric or developmental diagnoses was recruited from pre-existing data from our pediatric TMS research program. Subjects were matched as closely as possible by age and sex, blinded to neurophysiological results. Control subjects have a documented full-scale IQ > 80 (for participants under age 12 years) or are enrolled in regular classrooms with no special educational needs. Absence of psychiatric or developmental disorders is confirmed by structured diagnostic interviews as well as direct interview by board certified pediatric neurologists and psychiatrists on the research team. Based on previous neurophysiology experiments including paired pulse TMS measures in TDC and ADHD15. In addition to ADOS testing, cognitive testing results (IQ) were obtained from previous clinical or research tests (within the past 5 years) performed at our institution or from psychological assessments from the patient’s school and represent scores from the Stanford-Binet16, Wechsler Intelligence Scale for Children17, or Wechsler Adult Intelligence Scale18.On the day of the study visit, the primary study physician, performed both a medical history and physical evaluation, reviewed the previous baseline testing and confirmed the diagnosis of ASD based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria19, the Vineland Adaptive Behavior Scales-2nd Edition (VABS-II)20, Conners Parent Rating Scale–Revised 21, and Social Communication Questionnaire (SCQ)22 to assess adaptive behavior, problem behaviors and competencies, attention, health, and social behaviors. The Physical and Neurological Exam for Soft Signs (PANESS) was also used to assess subtle soft neurological signs and motor skills in children that had demonstrated aberrance in ASD and ADHD23.On the day of the study, parents or guardians were asked to complete the Social Responsiveness Scale (SRS)To be co-diagnosed with ADHD (ASD+), the patient needed to have met (1) DSM-5 criteria for ADHD Combined presentation (314.02), ADHD Predominantly inattentive Presentation (314.00), or ADHD Predominantly hyperactive/inattentive presentation (314.01) and (2) have impairment in a moderate or severe level of function attributed to the ADHD symptoms as understood by clinical history and standardized measures . In our matched TDC sample, subjects were excluded if they were suspected of ADHD , diagnosed with a developmental or other neuropsychiatric disorder, or reported ADHD diagnoses among first-degree relatives.24. KiTAP is an easy to follow computer-assisted test battery for non-verbal attentional functioning across several key domains and has been validated in a neurodevelopmental population25. Subtests were performed in the following order: Alertness, Distractibility, Flexibility, and Go/No Go. Following previous methods in a lower-functioning population25, the shortest subtests will be administered first to give the widest number of children the greatest chance to complete an entire subtest.Attentional function was assessed using the KiTAP26. Electromyography (EMG) was recorded from the dominant first dorsal interosseous (FDI) muscle with surface electrodes, amplified, filtered (100/1000 Hz)  and stored for analysis using Signal® software and a Micro1401 interface , as we have described previously12. Initially, single pulse was performed using a 90-mm circular coil (anterior posterior orientation) over the dominant primary motor cortex to elicit a motor evoked potential (MEP) and to determine active and resting motor thresholds  using standard methods27. A site for stimulation was marked using a wax pencil that could produce clear MEPs with the lowest possible stimulation intensity.TMS experiments were performed using two Magstim 200 stimulators  connected through a Bistim module to a 90-mm circular coil. Circular coils produce similar qualitative patterns of inhibition and facilitation from motor cortex stimulation29. Thus, as in our previous studies, we elected to use a slightly less efficient conditioning pulse that would yield a greater spread of ratios in children leading to a higher opportunity to investigate group differences of interest and maintain the ability to make comparisons with our previous datasets. SICI and ICF were expressed as a ratio of the mean peak to peak MEP amplitude produced by the 3 ms and 10 ms ISI, respectively, to the mean peak to peak MEP amplitude produced by the test pulse condition alone. Next, cortical silent period (CSP) was measured following administration of six pulses at 150% of AMT during a moderate FDI muscle contraction . Average CSP was calculated from the average of onset and offset of the silent period of each of 6 rectified EMG tracing30. A 16-point review of systems was used pre and post TMS to assess for any adverse effects32.Subjects (primarily due to younger age and motor cortex immaturity) who necessitated greater than 100% stimulator output to measure either threshold or single/paired pulse were excluded from additional TMS measures. After establishing individual subject AMT and RMT, thirty trials of single or paired pulse stimulation at an interval of 6-7 s with one of three conditions in pseudorandom order: (1) single (test) pulse, (2) paired pulse (condition/test) at an interstimulus interval (ISI) of 3 milliseconds, and (3) paired pulse (condition/test) at an ISI of 10 milliseconds were administered. The test pulse was set at 120% of participant RMT and the conditioning pulses set at approximately 60% of RMT. Previous work in our laboratory found that conditioning pulses greater than 60% led to stronger inhibition in clinical and control youth compared to “dose” intensity curves in healthy adultsp-values were calculated using Tukey post-hoc analysis. If Levene’s Test of Homogeneity of Variance was violated, then a one-way Welch ANOVA was conducted and adjusted p-values were obtained following Games-Howell post-hoc testing. Clinical measures , computerized testing , motor function , and demographics were compared between ASD− and ASD+ using a series of independent t-tests. The Holm method was implemented to generate adjusted p-values to correct for multiple comparisons between groups. Pearson’s correlation coefficient (r) or Spearman rank coefficient  was used to determine associations between TMS measures and behavioral data and stratified by diagnosis. To control for type I errors, we implemented the false discovery rate procedure33 to calculate adjusted p-values from the correlation matrix of demographics, clinical, and TMS physiology measures.All statistical analyses were performed with SPSS version 24  and R version 3.6.0. Analysis code and deidentified datasets are available on request. All data were assessed for normality with the Shapiro-Wilk test. The primary outcome of this study was a comparison of TMS evoked measures  between ASD−, ASD+, and TDC groups using a one-way ANOVA and adjusted The final sample for analysis included 49 ASD subjects  and 29 ASD+  and 24  matched TDC subjects for TMS measures. No significant differences in age, gender, or handedness were identified between groups. Demographics and clinical measures are presented in Table n = 8, 40%) and antipsychotics  while children in the ASD+ group were taking the following medications: selective serotonin reuptake inhibitors ; antipsychotics ; stimulants ; and alpha-2 agonists .Children in the ASD− group were taking the following other medications: selective serotonin reuptake inhibitors . ICF ratio was higher in the ASD- group (1.27 ± 0.36) and TDC (1.18 ± 0.24) compared to the ASD+ group (1.01 ± 0.19). Games-Howell post-hoc analysis revealed that ASD+ was reduced compared to both ASD−  and TDC ), p = 0.018). No significant difference between magnitude of MEP raw amplitudes (mV) were identified between ASD− and ASD+ groups for single test pulses with no conditioning pulse , 3 ms ISI conditioning pulse , and 10 ms ISI conditioning pulse .Comparisons of resting motor physiology data from ASD+, ASD−, and TDC subjects are shown in Table n = 17) and temporarily discontinued for the study and subjects untreated with stimulants (n = 12) did not have significantly different ICF (p = 0.33). Treated (p < 0.039) and untreated (p = 0.007) ASD+ groups both had significantly decreased ICF compared to the TD group. Correlations with resting motor physiology are presented in Table ICF was significantly diminished in ASD+ children compared to both ASD− and TDC youth Fig. . Effect CF p = 0.3. Treatents n = 1 did not Despite the high prevalence and clinical significance of ADHD co-occurrence in ASD, little is known regarding the underlying neural mechanisms that engender behavioral differences between ASD+ and ASD−. Intracortical interneurons within the motor cortex receive afferent inputs from other brain regions and play a critical role in modulating motor activity. MEP amplitude as a function of single and paired pulse TMS has been used as a surrogate marker for physiological changes associated with certain cognitive processors or disease states. Our primary interest was examining the clinical and behavioral correlates of motor cortex excitability differences, if any, across a large cohort of youth stratified by ADHD co-occurrence. We report a novel and robust finding, that ICF, a faciliatory TMS evoked paradigm, is reduced in ASD+ compared to ASD- and control subjects. Further, a decrease in ICF was related to worsening clinical and task-based measures of inattention and executive function.36. SICI has been associated with γ-aminobutyric acid (GABA)A mediated inhibition at the level of the cortex37 and is associated with cortical interneuron activity. This finding supports a network model in which ADHD features are the product of dysfunctional GABAergic inhibition preventing the refinement of signals entering the motor cortex40 Thus, considering overlapping clinical presentations between ASD+ and ADHD, our expectation was to identify similar SICI changes associated with ADHD symptoms. Thus, we were surprised to see a marked reduction in ICF, without SICI differences, within the ASD+ group with clinically relevant associations.Prior work by our group has established a robust relationship between reduced SICI, but not ICF, with increased ADHD severity and aberrant motor development across several cohorts of clinical populations including ADHD and Tourette’s syndrome with ADHD29. Single-dose pharmacology studies have associated the ICF phenomena to excitatory, NMDAR-mediated neurotransmission in the cortex37. Most, but not all, previous studies in ADHD have shown no difference in ICF41. In addition to impaired SICI, Buchmann et al. also found an impaired ICF (ISI = 13 ms versus 10 ms in the present study) in an ADHD cohort of children compared to controls42. They further demonstrated a restoration of ICF following MPH administration. Similar studies in adults with ADHD are limited by sample sizes and conflict with existing pediatric studies. One out of two studies found a decrease in SICI and neither study found any difference in ICF44.Thus, in the present sample, the ASD+ finding of an impaired ICF with an intact SICI represents a distinct profile among studied neuropsychiatric disorders in children41.ICF is a TMS paradigm in which a subthreshold (60% of RMT) conditioning stimulus is paired with a suprathreshold (120%) test stimulus with an interstimulus interval of 10–15 milliseconds. This typically results in a higher amplitude (facilitation) than the test pulse alone45. In contrast, SICI appears to be more strictly a phenomena isolated to the cortex46. Thus, the variability in ICF, even within similar individuals or disease -states, may additionally reflect dynamics related to deeper brain structures. Indeed, our ASD− and ASD+ cohort were finely phenotypically matched in terms of cognition, social functioning, and language abilities but differed widely in terms of inattention and hyperactivity symptoms. In youth with ADHD, a recent diffusion tensor imaging (DTI) study highlighted reduced grey matter concentration in the head of the left caudate as well as microstructural abnormalities in white matter density in the left middle temporal gyrus, right internal capsule, corpus callosum, and right midbrain47. Di Martino et al. explored functional neuroimaging correlates of ASD− and ASD+ youth by studying resting state fMRI local and global connectivity48. Diagnosis of ASD (regardless of the presence or absence of ADHD co-occurrence) was associated with abnormalities in the temporal lobe and amygdala. On the other hand, in ASD or TDC subjects with high levels of ADHD symptoms they identified increased local connectivity, associated with developmental immaturity, exclusively in subcortical structures including the right caudate, pallidum, and putamen. This local connectivity measure was also associated with CPRS total scores. The centrality of basal ganglia circuitry in ADHD has robust support in the literature and along with our observations may have a conserved underlying mechanism across disorders in which ADHD symptoms are present. Taken together, these findings suggest that ICF may reflect the aggregate effects of several aberrant subcortical neural circuits specifically relevant to ADHD symptoms in the ASD population.Though previously ICF has been characterized as an exclusively cortical phenomena, recent reports have provided compelling evidence that ICF results from a complex interaction of cortical and subcortical circuits49. Thus far, there is emerging evidence that ICF is positively associated with [1 H] MRS measures of glutamate51. Dyke et al. studied the association of ICF with Glu/tCr levels in a 7 T [1 H] MRS. ICF demonstrated “substantial evidence” for a positive linear relationship with Glu/Cr levels using Bayesian classification . No other paired pulse TMS measure in the study resulted in significant (or near significant) associations. Thus, our findings may partially explain a potential confounding factor in MRS studies which ASD is characterized without consideration for ADHD co-occurrence. For example, a recent meta-analysis of [1 H] MRS activity found that across studies, none of which accounted for ADHD co-occurrence in ASD, glutamate and Glx level varied widely based on age group or cortical region with no consistent behavioral correlates52.The marked difference in ICF between ASD+ and ASD− may have special relevance for interpretation of [1 H] MRS studies of ASD. Spectroscopy studies of neurotransmitter levels in ASD remain nuanced and conflicting at times54. Differences have emerged when ASD subjects are stratified by subgroups (thus limiting power) such as by early language delay56. The current study represents one of the largest samples of ASD youth to undergo TMS characterization (n = 59) and supports previous findings of essentially normal SICI. Thus, we opted to identify, if any exists, clinical characteristics that correlated with SICI and CSP values. Increased SICI (higher inhibition) and lengthened CSP, were highly correlated with the CPRS Learning Problems score reflecting academic struggles in reading, spelling, and/or math57. Since SICI was in the normal range, it is hard to interpret these findings without further study, including characterization of task-based SICI58.Previous TMS investigations in heterogeneous samples of ASD subjects have not elicited differences in SICI or ICF13. The present findings suggest that based on TMS measures, there is a disassociation between ASD+ and ADHD such that, on average, ASD+ display intact SICI and elevated ICF and ADHD youth have reduced SICI and inconsistent ICF findings. This distinction may indicate that insufficient surround inhibition provided by GABAergic interneurons associated with SICI may be more central to attention and hyperactivity symptoms in ADHD, but other brain connectivity dysfunctions may be more prominently involved in ASD+.We (and others) have previously reported that SICI is a marker for ADHD diagnosis and severity in otherwise typically developing youthWe also report on several significant disease-relevant clinical and behavioral correlations with TMS measures. Diminished ICF is associated with worsening CPRS Inattention, CPRS Executive Function, ADHDRS-IV Inattentive Percentile, and ADHDRS-IV Total Percentile scores. The direction of this effect is internally consistent with the significant group difference between ASD− and ASD+. ICF did not correlate with subscales of hyperactivity or measures of social function. Though weakly correlated, lower ICF was related to worse attention and flexibility reaction times on computerized testing. However, these findings did not survive FDR correction and should be interpreted with caution. We speculate that one explanation, which would encourage further study into these preliminary findings, may relate to the nature of inattention in ASD versus ADHD. Despite widely different clinical severity in ADHD symptoms, both ASD− and ASD+ performed similarly on our computerized continuous performance battery.59. Thus, performance on traditional CPT testing may reflect attentional domains more reflective of ADHD than inattention that may be due to special interests or social functioning in ASD.In ASD, attention may represent difficulty in being able to flexibility shift attentional focus to non-preferred activities versus a the struggle to focus on the same matter over time as seen in ADHD41. Thus, we opted to apply identical methods, equipment, and stimulation parameters to maximize the extent our current results can be comparable to previously published results. Subject selection remains a challenge in pediatric TMS subjects. Higher resting thresholds in younger subjects made traditional paired pulse TMS measures using currently available stimulators impossible. As we have previously explored theta burst stimulation in the pediatric population, we are investigating methods of studying cortical excitability using subthreshold stimulation intensities, which may expand the inclusion pool to younger subjects. Finally, the inclusion of medication patients will likely have an effect across measurements including neuroleptics and SSRIs affecting ICF37. However, with current estimates demonstrating polypharmacy among autistic youth, a med free population would severely limited recruitment (and introduce other forms of selection biases)60.Significant limitations to this study include the subjective nature of parent rating scales and the inherent heterogeneity within the ASD sample. However, the TMS measures are objective and quantitative and were performed blinded to the diagnostic group to minimize bias. As our primary focus was to identify the distinct resting motor physiology in ASD attributable to ADHD co-occurrence, our study cohort was limited to ASD subjects. To date, many groups (including our group) have reported paired-pulse TMS measures in ADHD and control subjects sufficient for meta-analysis42 and in controlled trials in healthy subjects63. Recent studies have implicated impaired glutamate signaling in some cases of ADHD and may represent an emerging treatment target. Thus, future study of acute MPH challenge may further clarify the relationship of TMS measures to treatment response. In addition, the current work is isolated to the motor cortex, but it is possible that specific abnormalities may exist upstream in other brain regions. Future studies may consider additional modalities including joint EEG/TMS to better understand other cortical contributions to the current findings.The treatment implications of this finding will need further consideration. Of relevance, however, methylphenidate (MPH) administration, a highly efficacious treatment for ADHD, is frequently associated with increased ICF in ADHDADHD remains one of the most common and distressing co-occurring diagnoses in ASD with significant impact on prognosis. In the present study, we report a reduction of ICF in ASD+ from ASD− and TDC (and intact SICI). Along with recent discoveries regarding the physiology of ICF and association glutamatergic and subcortical circuits suggest that ADHD co-occurrence in ASD may have a distinct electrophysiological “signature”. ICF may constitute an emerging biomarker to study the physiology of ADHD in ASD, which may align with disease prognosis or treatment response.Supplementary Table


Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood worldwide, and causes significant impairments in overall functioning. In order to develop effective prevention and intervention programs, knowledge of the determinants that have an impact on the onset and development of ADHD symptoms is essential. So far, little is known about factors affecting ADHD symptoms in children and adolescents over time. Therefore, this study investigates potential psychosocial risk and protective factors for ADHD symptoms based on cross-sectional and longitudinal data of a German population-based study.n = 1,384 aged 11 to 17 years) were collected at three measurement points  covering a period of two years. We used latent growth modelling to investigate effects of parental mental health problems (risk factor) and self-efficacy, family climate and social support (protective factors) on symptoms of ADHD based on cross-sectional as well as longitudinal data. Sociodemographic factors, pre- and postnatal factors, and comorbid symptoms of internalizing and externalizing mental health problems were considered as covariates.Data on children and adolescents (At baseline, male gender, younger age, stronger aggressive behavior, and stronger parental mental health problems were related to more ADHD symptoms. Longitudinal analyses showed that female gender, migration status, increasing symptoms of generalized anxiety, increasing aggressive behavior and increasing parental mental health problems were associated with stronger increase of ADHD symptoms over time. However, improving family climate was related to decreasing ADHD symptoms over time. We further found moderator effects for social support.The findings of the study provide important information concerning risk and protective factors in the context of ADHD. Hence, the results may be integrated into the planning and implementation of future prevention and early intervention strategies that target affected children and adolescents. Attention-deficit/hyperactivity disorder (ADHD) as defined by DSM-5 and ICD-10 is characterized by symptoms of inattention, hyperactivity and/or impulsivity that are present in different settings and cause significant impairments in social and academic functioning . ADHD isThe etiology of ADHD is considered a multifactorial process, whereby the genetic predisposition and biological vulnerabilities are assumed to be most important , 10. ComThe summarized findings highlight the importance of effective prevention and intervention programs for children suffering from ADHD in order to prevent the onset of ADHD symptoms and the associated long-term consequences. In this context, knowledge of the determinants that have an impact on the onset and the development of ADHD symptoms over time is essential. Within research on mental health, the investigation of risk and protective factors has become increasingly important in recent years . While rOnly few studies have investigated the effects of psychosocial risk and protective factors in the context of ADHD so far. Cross-sectional studies found protective effects of personal factors such as a high sense of coherence  and selfIn addition, parental mental health problems have demonstrated to be a well-established risk factor for child psychopathology in general , 32–35 aApart from the psychosocial risk and protective factors mentioned above, further studies reported for instance that pre- and postnatal factors such as premature birth, low birth weight and maternal substance use during pregnancy are associated with a higher risk of ADHD , 39. BesThe current state of research, however, demonstrates a lack of longitudinal studies on personal, familial, and social determinants for the development of ADHD symptoms. In particular, comparatively little information is available from population-based studies on risk and protective factors affecting the change in children’s and adolescent’s ADHD symptoms over time. Therefore, the current study aims to add to the existing literature by investigating the cross-sectional as well as longitudinal influences of risk and protective factors on the development of childhood ADHD symptoms. Based on the above mentioned findings, we expected that parental psychopathology as risk factor, and self-efficacy, family climate and social support as protective factors not only have an impact on initial ADHD symptoms, but also on the development of ADHD symptoms over time. Since research has shown that risk and protective factors not only have a direct effect on an outcome, but tend to interact in different ways , the stun = 2,942 children and adolescents aged 7 to 17 years). These children, adolescents and their parents were informed about the BELLA study and asked to participate. Finally, n = 2,863 (response rate: 97.3%) children and adolescents (aged 7 to 17 years) and their parents gave their written informed consent and participated in the BELLA baseline assessment. To follow up the baseline sample, further measurement points of the BELLA study were conducted including a 1-year (2004 to 2007) and a 2-year follow-up (2005 to 2008). Of the BELLA baseline participants, n = 2,423 (84.6%) participated in the 1-year follow-up and n = 2,190 (76.5%) participated in the 2-year follow-up. Detailed descriptions of the KiGGS and the BELLA study have already been published [The longitudinal BELLA study is the mental health module of the German National Health Interview and Examination Survey for Children and Adolescents (KiGGS) . The BELublished , 44. Datublished .n = 1,384 children and adolescents (aged 11 to 17 years at baseline).In the present study, we analyzed data from the first three measurement points of the BELLA study  covering a period of 2 years. Participants aged 11 to 17 years at baseline could be included in the analyses, if i) relevant information gathered only at baseline were completely available , migration status, premature birth, and maternal smoking and alcohol use during pregnancy) and if ii) longitudinally measured data were available for at least one of the three measurement points . Further, cases were only analyzed if the same parent reported on parental mental health problems at each investigated measurement point in the BELLA study. The final sample under analysis included Age (in years), gender and the SES were determined at baseline. The SES was assessed using the parent-reported Winkler-index , which gParent-reported information on premature birth and maternal substance use  during pregnancy were gathered by means of items included in the paper-pencil questionnaire administered to the parents at baseline of the KiGGS study. Concerning premature birth, parents were asked if the child was born full-term (born in the period of three weeks before and two weeks after the predicted birth date), post-maturely or pre-maturely. For the presented analyses, item responses were collapsed resulting in a dichotomous score (0 = “full-term or postmature birth” and 1 = “premature birth”). For maternal substance use during pregnancy, two items  were presented with three response options each . Responses were collapsed resulting in dichotomous scores for maternal smoking as well as for maternal alcohol use during pregnancy .Symptoms of ADHD in children and adolescents were measured at each investigated measurement point based on a parent-reported Conners Global Index (C-GI) , 50. A GComorbid symptoms were assessed at each investigated measurement point. Depressive symptoms in the children and adolescents were measured using the self-reported German version of the established Center for Epidemiologic Studies Depression Scale (CES-DC) , 55. By Self-reported symptoms of generalized anxiety in children and adolescents were assessed based on a German version of the Screen for Child Anxiety Related Disorders (SCARED-D) –58. The Parent-reported aggressive and dissocial behaviors in children and adolescents were assessed based on the German version of the well-established Child Behavior Checklist (CBCL) , 60. ThiThe Symptom-Check List 9-item Short version (SCL-S-9)  served tProtective factors were measured at each investigated measurement point. To measure self-reported self-efficacy in children and adolescents, the General Self-Efficacy Scale (GSE) , 64 was The family climate was assessed in children and adolescents based on the German Family Climate Scale (FCS) . The FCSSelf-reported social support in children and adolescents was measured by means of the German translation of the Social Support Survey (SSS) . Items oWe used latent growth modelling to analyze our cross-sectional and longitudinal data. This analyzing approach is used in social, psychological and health research frequently; it is especially recommended for analyses of change in behavior . By meanIn the present study, data analyses followed a two-step procedure. At first, we calculated an LGM for each longitudinally measured construct under analyses . Subsequently, we used intercepts and slopes resulting from LGMs in linear regression models. Regression Model A0 served to investigate whether initial symptoms of ADHD were predicted by initial parental mental health, self-efficacy, family climate and social support. Regression Model B0 was used to analyze if the change in ADHD symptoms was predicted by the initial state of parental mental health, self-efficacy, family climate and social support, as well as by the change in these variables over time. The following covariates were added to Models A0 and B0: i) sociodemographic variables , ii) information on premature birth, and maternal smoking and alcohol use during pregnancy, and iii) comorbid symptoms of depression, generalized anxiety, aggressive and dissocial behaviors . For each regression model, all included variables were entered simultaneously into the model.Moreover, we aimed to explore whether potentially protective factors moderate the relationship between the risk factor parental mental health and ADHD symptoms in the children and adolescents. For this purpose, we additionally included interaction terms in our linear regression models. We thus examined if the association between initial ADHD symptoms and initial parental mental health problems was moderated by initial self-efficacy, initial family climate or initial social support (Model A1). Additionally, we investigated whether the associations between the change in ADHD symptoms and the initial state of parental mental health as well as the change in parental mental health were moderated by self-efficacy, family climate or social support (Model B1).For all regression models, we centered metric variables. To evaluate the strengths of detected effects, we followed Cohen’s rules of thumb : a standn = 1,384 children and adolescents aged 11 to 17 years at baseline is described in n = 306; medium SES: 51%, n = 706; high SES: 27%, n = 372), and 4% of the analyzed children and adolescents had a migration status. Questions on parental mental health problems for each investigated measurement point were answered by the mothers of 92% of the participating children and adolescents , by the fathers of 7% (n = 103) and by step-, foster- or grandparents for 1% (n = 7) of the participants. Concerning items on premature birth and maternal smoking and alcohol use (administered at baseline in the KiGGS study), for 89%  of the participants the mothers, for 8% (n = 107) the fathers, and for 3% (n = 37) mother and father together responded (for n = 3 cases foster-/adoptive- or grandparents responded and for n = 4 cases information was missing).The analyzed sample including Correlations between the score of the C-GI scale restless-impulsivity and the single items of the SCL-S-9 ranged from .15 to .29 based on baseline data. Small associations were found for psychoticism (r = .15), somatization (r = .16), phobic anxiety (r = .16), paranoid ideation (r = .16), obsessive-compulsive (r = .22), and anxiety (r = .24); nearly moderate associations were detected for hostility (r = .25), interpersonal sensitivity (r = .28), and depression (r = .29).Results for Model A0 see  based onBased on longitudinal data, results for Model B0  showed fFurther regression models were conducted to explore moderator effects of potentially protective factors on the relationship between parental mental health problems (risk factor) and ADHD symptoms. Results are provided as Supplementary Information  were associated with more ADHD symptoms in children and adolescents. This result is in line with previous studies on parental psychopathology and child mental health , 32–35 aContrary to our assumption and to former research , 27, 28,Moreover, although we could not find any direct effects of the protective factors self-efficacy and social support on ADHD symptoms in our cross-sectional and longitudinal analyses, we detected moderating effects for social support on the relationship between parental mental health problems and children’s and adolescent’s ADHD. Increasing social support over time could attenuate the association between strong parental mental health problems and strong ADHD symptoms. Corresponding moderator effects were found by Klasen et al.  in theirIn our moderator model based on longitudinal data, we further unexpectedly found an association between increasing ADHD symptoms and increasing social support over time only in children of parents with less severe mental health problems. This finding may at least partly reflect the direct and rather immediate supportive reaction of a healthy social environment on ADHD symptoms in children and adolescents.Rose et al.  suggesteBased on baseline analysis, we further detected that male gender and younger age were associated with stronger symptoms of ADHD in line with results from previous population-based studies , 6. ThisRegarding the examined comorbid mental health problems, we found a strong association between externalizing symptoms of aggressive behavior and ADHD symptoms, confirming results from previous studies investigating this relationship , 12. YetBased on the analysis of longitudinal data, we further found that the increase of ADHD symptoms over time was more pronounced in girls compared to boys, which could be explained by the fact that girls are faced with different challenges during the transition from childhood and adolescence to young adulthood compared to boys. Moreover, children and adolescents with migration background also experienced a stronger increase of ADHD symptoms over time compared to youths without migration background. This is of particular concern for prevention and intervention efforts in migrant populations since data from the nationally representative KiGGS baseline study suggest that there might be migrant-specific barriers to the use of health care services, in particular for families with children and adolescents with ADHD symptoms . FurtherThe present study has the following limitations. By means of the variables included in our baseline model, a proportion of 52% of the variance in ADHD symptoms was explained. However, we could only explain 7% of the variance in the slope of ADHD symptoms by the variables included in our longitudinal model. Further, we detected only small effects in our longitudinal model, if effects were given at all. This may be due to the fact that we investigated a general population sample with consistently rather low levels of mental health problems, and with rather good self-efficacy, family climate, and social support. Further, our study only covered a period of two years. This issue is reflected in the fact that the slopes for some investigated constructs did not vary significantly across individuals . Future research may cover a longer period of time. However, these findings may as well indicate that the development of ADHD symptoms is associated with important factors that we did not consider in our model based on longitudinal data. These factors may include genetic risks , 10, advOur study has several strengths. Data was derived from the BELLA study, which is the first comprehensive longitudinal study to assess the mental health and well-being of children and adolescents in Germany. The strengths of the BELLA study include the large population-based cohort and the wide age range of the participants. Further, risk and protective factors were assessed using established measures. Self-reported data of children and adolescents was used to assess the protective factors and comorbid symptoms of internalizing problems. Parent-reported data was used to assess parental psychopathology, symptoms of ADHD and comorbid aggressive and dissocial behaviors since research has shown that externalizing disorders are better observable by parents , 92. MorTo the best of our knowledge, this is one of the first studies to investigate the effects of personal, familial and social risk and protective factors on ADHD symptoms in children and adolescents over time. The results of our study indicate that parental mental health problems can have detrimental effects on ADHD symptoms, while a good family climate and social support can have beneficial effects on ADHD in children and adolescents over time.Given that ADHD is highly prevalent and causes significant impairments in almost all areas of life, our findings have important implications for prevention and clinical practice. Besides family-based interventions, future prevention and early intervention programs should focus on supporting the availability of good social support and on enhancing social skills, particularly in children of mentally ill parents, in order to reduce risks and prevent the onset of ADHD symptoms.S1 FileS1A Table. Protective factors self-efficacy, family climate, and social support moderating the relationship between parental mental health problems and symptoms of attention-deficit/hyperactivity disorder in children and adolescents.(DOCX)Click here for additional data file.S2 File(XLS)Click here for additional data file.


Attention deficit hyperactivity disorder (ADHD) is one of the most common behavioral disorders in childhood with long-term outcomes. Although ADHD is the most studied behavioral disorders of childhood in developed countries, few studies have been conducted in Ethiopia. The aim of this study was to determine the prevalence of ADHD in rural parts of Ethiopia.  A cross-sectional study was conducted from May to June 2015 among children aged 6 to 17 years living in rural areas. A multistage cluster sampling technique was used to select 1302 participants. The Disruptive Behavior Disorder Rating Scale was used to collect the data. Logistic regression analysis was used to see statistically significant variables.  The prevalence rate of attention deficit hyperactivity disorder (ADHD) among children was 7.3%. Being male (Adjusted Odds Ratio (AOR) = 1.81, 95% CI: ); living with a single parent ); child birth order/rank ); and low family socioeconomic status ) were significantly associated with ADHD.  The ADHD prevalence rate was found to be similar with global reports. Prevention and early management of maternal complications is important to reduce the prevalence of ADHD among children. Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with childhood onset . ADHD isThe worldwide pooled prevalence of ADHD for children aged 18 years and below was 7.2% from the systematic review and meta-analysis of 175 studies worldwide . From 86Studies in Africa are limited, but ADHD was reported in certain studies. In Africa, the prevalence of ADHD ranges between 5.4% and 8.7% among school children and 1.5% in the general population. However, it ranges from 45.5% to 100.0% among special populations of children with possible organic brain injury . The preADHD is a result of complex dealings between genetic, environmental, and developmental traits, and genetic factors are credited for determining about 80% of the cases . The maiChildren with ADHD are at risk of dropping out of school, becoming pregnant as a teenager, and committing criminal behavior. Untreated ADHD increases the risk for future complications such as poor academic performance and learning delay, low self-esteem, poor social skills, and increased susceptibility to physical injury in childhood . ADHD usThe study was conducted in the Girja district, which is found in the Guji zone, Southern Oromia regional state, Ethiopia. It is located in the southern part of Ethiopia, which is 559 kilometers from Addis Ababa. It has a total population of 62,083 , 12,934 households, 19,357 children with ages from 5 to 15 years, and 20 kebeles .A study using a community-based cross-sectional design was conducted among children aged 6 to 17 years old living in rural areas of the Girja district, Guji zone, Oromia regional state, Ethiopia, from May 09 to June 02, 2015. Study populations were randomly selected and children aged 6 to 17 years who lived in the study area for at least six months prior to the study were targeted. All children aged 6 to 17 years old living in the rural area of the Girja district in the selected kebeles  were selected as the study population. Children aged 6 to 17 years old who had informants present during the study period and children with a permanent residence in the Girja district were included. Children without informants were not included. We included 1302 children for the study.A multistage cluster sampling technique was employed. The twenty kebeles  in the district were classified as a cluster. Out of the twenty kebeles, five kebeles were selected by simple random sampling. A total of 5351 children aged 5 to 15 years old were living within 3573 households in the five selected kebeles. From each kebele, two Gotts  were selected randomly. Then, we selected children aged 6 to 17 years old from each household which had an eligible child. When more than one eligible child was available in the household, the lottery method was used to select one child.The presence of ADHD among children aged 6 to 17 years old was assessed by using the Disruptive Behavior Disorder Rating Scale based on the DSM-IV criteria . The scaP value less than 0.2 during bivariate analysis were entered into multivariable logistic regression. The strength of the association was presented by crude odds ratio and adjusted odds ratio with their 95% confidence interval (CI). Variables that have P values less than 0.05 were considered statistically significant.First, the data was checked for completeness and consistency. Then, it was coded, entered, and cleaned before and during data processing by using EpiData version 3.1 and exported to Statistical Package for the Social Sciences version 20 for analysis. Association between the dependent and independent variables was assessed using logistic regression. Variables with a Ethical clearance was obtained from the Ethical Review Committee of the University of Gondar and formal permission letters were taken from the Girja district administration and health offices. Written assent was taken from the parents/guardians and informed consent was taken from the mothers/guardians for their voluntary participation. Confidentiality was maintained by omitting personal identification. Children with ADHD were referred to the psychiatric clinic.From the 1302 proposed participants, 1238 children were involved with a response rate of 95.1%. There was a total of 640 males (51.7%), and 894 (72.2%) of the children were between the ages of 6 and 12 years. Majority of the children, 1208 (97.6%), were Oromo by ethnicity and 1003 (81%) were Protestant by religion .Majority of the parents, 1202 (97.1%), were married and 36 (2.9%) were divorced/widowed. From the total number of mothers, 716 (58.5%) could not read and write. Regarding family size, 68.3% (845) of the families had greater than four children in the house. Of the children, 685 (55.3%) were the first child in birth order/rank .Other clinical- and maternal-related factors are listed in The prevalence of ADHD was 7.3% (90) . From this, 57 (63.3%) were from the ADHD-IN subtype, 22 (24.5%) were from the ADHD-HI subtype, and 11 (12.2%) were from the ADHD-C subtype. ADHD subtypes clustered by sex are shown in P value at 0.2 significance). Those independent factors were entered into multivariate regression for further analysis. During the multivariate analysis of all the explanatory variables of ADHD, it was found that being male ), child living circumstance (living with single parent) ), child birth order/rank ), and family socioeconomic status ) were significantly associated with ADHD , and maternal health status during pregnancy fulfilled the minimum need (ith ADHD .The behavioral disturbance of children is the main hallmark that indicates children's understanding of the world. Behavioral disturbance was found to have a high implication for their social, academic, or occupational functioning. ADHD has a severe behavioral implication to the cognitive and social development among children. The persistent behavioral disturbance in children with ADHD may lead to major psychiatric disorders during adulthood.The findings of the present study on the prevalence rate of ADHD are consistent with the findings of the studies done in Egypt, which was 6.9% ; in KenyGender was associated with ADHD, and males were almost two times more likely to have ADHD than females. This explanation agreed with several other studies , 16, 20.It was found that the first-born child was more than two times more likely to develop ADHD than the second and above child in the birth order in this study. This was similar with the study conducted in Egypt . The reaThe child of a family with a low-level economic status was more than two times more likely to develop ADHD than a child of a family with a high-level economic status. This was in line with several previous studies , 20, 40.The Disruptive Behavior Disorder Rating Scale is not validated in the Ethiopian setting. Recall bias may be present for childhood health status before six years old, child feeding style in the first six months of life, maternal health status during pregnancy, and complication at delivery.The prevalence of ADHD in Ethiopia is similar to the prevalence of ADHD in most African countries and worldwide. ADHD is associated with sex, living with single parents, child birth order/rank, and low socioeconomic status of the family. Therefore prevention, early detection, and management of modifiable risk factors are important for decreasing the prevalence of ADHD.


Several attempts have been made to reduce the harmful side effects and increase the efficacy of current drugs used to treat attention-deficit/hyperactivity disorder (ADHD). Many articles have studied medicinal herbs as an effective supplement in treating ADHD. In a similar manner, this study provides foundational data to identify herbs that are potentially effective in treating ADHD by text mining of Donguibogam, which is a comprehensive summation of the important traditional principles and practices of Korean medicine. Text mining was performed for 3833 herbal prescriptions and 1108 medicinal herbs comprising prescriptions listed in Donguibogam. The first step was frequency analysis followed by chi-square test, which is a statistical hypothesis test. Twelve medicinal herbs were selected for each ADHD subtype: hyperactivity ADHD type (ADHD-PHI) and attention-deficit ADHD type (ADHD-PI). Compared to previous research on traditional literature, a newer and more efficient methodology of selecting herbal medicines was developed in this process. Patients with attention-deficit/hyperactivity disorder (ADHD) present with symptoms such as lack of concentration, impulsiveness, and excessive activity, and ADHD is one of the neurobehavioral disorders commonly occurring in childhood. .Currently, approximately 5-7% of school-aged children and 2-5% of adults are believed to have ADHD worldwide . In the Both genetic and environmental factors affect the occurrence of ADHD, but the exact cause has not yet been elucidated. However, some researchers have found that symptoms of impulsiveness and attention-deficiency tend to be present when the function of the frontal lobe of controlling thought and concentration decreases. Another possible reason could be a problem in metabolizing neurotransmitters such as catecholamine, norepinephrine, and dopamine . TherefoThus far, traditional Korean medicine or traditional Chinese medicine research related to ADHD has been mainly composed of research based on original literature, systematic review based on search treatises, and experimental research testing the treatment effect of herbs on ADHD –21. HoweFurthermore, this study considered hyperactivity ADHD type (ADHD-PHI) and attention-deficit ADHD type (ADHD-PI) as two separate disease symptoms. DSM is the most typical diagnostic method of ADHD, which classifies ADHD into three subtypes: predominantly inattentive (ADHD-PI), predominantly hyperactive-impulsive (ADHD-PH), and combined type (ADHD-C) . In WestThe herbal prescriptions deemed potentially effective for ADHD in Donguibogam (ADHD PEHP) were compared to the remaining herbal prescriptions in the book using the chi-square test, a statistical method used to determine homogeneity between two groups for a specific variable. Thus, the authors were able to select only those herbs showing statistically significant efficacy.First, all prescriptions in Donguibogam were extracted, and the herbs comprising each prescription were listed. In this process, medicinal herbs with synonymous names were standardized under one name.http://www.koreantk.com/ktkp2014).Second, Korean medical symptom names corresponding to ADHD-PHI and ADHD-PI were defined using existing research, and prescriptions and herbs containing at least one of these symptom names were selected  23]. In. In23]. Symptom Names in Traditional Korean Medicine That Matched ADHDADHD-PHI: Jingji, zhengchong, zhanyu, xu fan, fan zao, yin xuhuo dong, and shanghan xu xuezheng (searched by Xu Xue)ADHD-PI: Jianwang, laojuan shang, qi fa, and yin xu neireThe frequency of occurrence was calculated for each medicinal herb in ADHD-PHI PEHP. Only those herbs that made up more than 2% of the total were selected as the experimental group .Among these, “Cinnabaris” was removed because it is a medicinal herb derived from minerals and its use is restricted. Additionally, the control group was selected by counting the number of times the herbs that were selected as the experimental group appeared in the rest of the Donguibogam prescriptions, except for ADHD-PHI PEHP. The same steps were repeated for the prescriptions potentially effective for ADHD-PI. Following this, a chi-squared test was performed to evaluate whether experimental group herbs were more effective in the treatment of ADHD-PHI or ADHD-PI in a statistically significant manner than control group herbs Figures  and 3 , Glycyrrhiza uralensis , Rehmannia glutinosa , Angelica gigas , and Panax ginseng   . Liriope platyphylla (L.P), Paeonia lactiflora (P.L), Ziziphus jujuba (Z.J), Coptis chinensis (C.C), Polygala tenuifolia (P.T), and Phellodendron amurense (P.A) for ADHD-PHI was statistically significant at 0.01 and that of Pinellia ternata (P.M) was significant at 0.05. The usage of Atractylodes lancea (A.L), Citrus reticulata (C.R), and Cnidium officinale (C.O) was not statistically significant for ADHD-PHI. In cases of L.P and C.O, Yate's continuity correction chi-squared value was used, while the Fisher's exact test was used for Z.J and P. T. Based on their chi-squared values, L.P (127.12), P.C (83.29), and R.G (47.41) were found to have been used specifically for ADHD-PHI (in that order of specificity). The chi-squared values of Z.J (349.09) and P.T (102.80) were very high, but the authors decided to exclude them from the ranking order of significance because 25% of the cells had an expected frequency lower than 5 , P.G (7.69%), G.V (6.35%), P.T (5.35%), and A.G (5.02%) .Compared to the rest of the prescriptions, other than those of ADHD-PI PEHP, the usage of P.C, P.G, P.T, A.G, A.M, R.G, A.C, L.P, and Z.J for ADHD-PI was statistically significant at 0.01 and that of G.V, C.R, and B.C was significant at 0.05. The usage of A.L and P.L was not statistically significant for ADHD-PI. For P.C, P.G, A.G, C.R, A.L, R.G, and P.L, Yate's continuity correction chi-squared value was used, while the Fisher's exact test was used for P.T, A.M, A.C, L.P, Z.J, and B.C. Based on their chi-squared values, P.G (52.90) and P.C (48.26) were found to have been used specifically for ADHD-PI. The chi-squared values of P.T (266.99), A.C (137.65), and Z.J (83.39) were high, but the authors decided to exclude them from the ranking order of significance because Fisher's exact test was used .By reviewing the results of the analysis, 12 out of the 156 herbs comprising the ADHD-PHI PEHP and 12 out of the 69 herbs comprising the ADHD-PI PEHP were determined to be significant for further experiments. Figures The authors presented the order of importance of selected herbal medicines using statistics . The aforementioned points indicated that the methodology of this study was more systematic and efficient in selection.To reflect the characteristic of symptomatic treatment in Korean medicine, the study was conducted separately for ADHD-PHI and ADHD-PI. The results show that 8 herbs  were selected with statistical significance for both subtypes of ADHD. However, 4 herbs  were significant only for ADHD-PHI, while the other 4  were significant only for ADHD-PI. For each subtype of ADHD, there were 4 herbs that did not apply to the other subtype, which validated the approach in Korean medicine to consider different symptoms as separate diseases.Since this study was an analysis of original literature, its limitation was that it needed experimental verification. However, this study remains insightful by presenting a more efficient text mining method using statistical analysis. This may be the stepping stone in the future to utilize more diverse statistical tools of analysis of original research in Korean medicine.P.C, G.V, R.G, A.G, P.G, L.P, P.L, Z.J, P.M, C.C, P.T, and C.O were selected as herbs showing a high probability of effectively treating ADHD-PHI. Simultaneously, P.C, P.G, G.V, P.T, A.G, C.R, A.M, R.G, A.C, L.P, Z.J, and B.C were selected as herbs with a high probability of effectively treating ADHD-PI.Furthermore, this study presented the chi-squared test as a new method for obtaining medicinal herbs that are useful for treating ADHD.