The etiology of autism spectrum (ASD) and Attention Deficit/Hyperactivity (ADHD) disorders are multifactorial. Epidemiological studies have shown associations with environmental pollutants, such as plasticizers. This study focused on two of these compounds, the Bisphenol-A (BPA) and Diethylhexyl Phthalate (DEHP). The major pathway for BPA and DEHP excretion is via glucuronidation. Glucuronidation makes insoluble substances more water-soluble allowing for their subsequent elimination in urine.Detoxification of these two plasticizers is compromised in children with ASD and ADHD. Consequently, their tissues are more exposed to these two plasticizers.We measured the efficiency of glucuronidation in three groups of children, ASD (n = 66), ADHD (n = 46) and healthy controls . The children were recruited from the clinics of Rutgers-NJ Medical School. A urine specimen was collected from each child. Multiple mass spectrometric analyses including the complete metabolome were determined and used to derive values for the efficiency of glucuronidation for 12 varied glucuronidation pathways including those for BPA and MEHP.(1) Both fold differences and metabolome analyses showed that the three groups of children were metabolically different from each other. (2) Of the 12 pathways examined, only the BPA and DEHP pathways discriminated between the three groups. (3) Glucuronidation efficiencies for BPA were reduced by 11% for ASD (p = 0.020) and 17% for ADHD (p<0.001) compared to controls. DEHP showed similar, but not significant trends.ASD and ADHD are clinically and metabolically different but share a reduction in the efficiency of detoxification for both BPA and DEHP with the reductions for BPA being statistically significant. The etiology of autism spectrum disorders (ASD) is believed to be multifactorial. One prominent hypothesis involves toxicant exposure acting upon genetically susceptible individuals. ASD has strong associations with both environmental factors and genetic components –7. It isThe present study focused on two of these compounds, the common plasticizers Bisphenol-A (BPA) and Diethylhexyl Phthalate (DEHP). BPA and phthalates plasticizers are moderate molecular weight, hydrophilic and relatively inert aromatic compounds. BPA is used in the manufacture of polycarbonate plastics, as an antioxidant in some plasticizers, in polyvinyl chloride (PVC) manufacture, and the Epoxy resins used to coat the inside of many food and beverage cans , 13, 14.The evidence for an association of neurodevelopmental disorders with exposure to plasticizers is primarily from epidemiological studies based on maternal urine analyses . MultiplWe propose that the linkage is a genetically determined compromised ability to detoxify the plasticizers BPA and MEHP. The major pathway for BPA and DEHP metabolism and excretion is via glucuronidation. The glucuronidation pathway makes a large variety of substances more water-soluble allowing for their subsequent elimination from the body upon urination. Most BPA and DEHP excretion is as the glucuronide with a small amount as the sulfate –25.The key step in the glucuronidation process is the transfer of glucuronic acid from uridine-5′-diphospho-α-D-glucuronic acid (UDPGA) to the target molecule which could be a hydroxyl, amine, carboxyl, sulfhydryl etc. by Uridine 5′-diphospho-glucuronosyltransferases (UGTs) –28. The Previously, we reported that the efficiency of glucuronidation of DEHP and BPA metabolites was lower in children with ASD . The objTo determine whether the association of compromised glucuronidation was specific to ASD or was more general applying to other childhood neurodevelopmental disorders by measuring the efficiency of glucuronidation for another neurological disorder. Attention Deficit/Hyperactivity disorder (ADHD) was selected as the other disorder because it has many outward similarities to ASD , 32. ADHGlucuronidation encompasses many different sub-pathways. The second objective of the present study was to ascertain whether the association of ASD with decreased glucuronidation efficiency was unique to a specific plasticizer sub-pathway or common to other variants of the glucuronidation pathway. Therefore, the present study examined the relationships of ASD and ADHD with a varied group of twelve glucuronidation sub-pathways. Five were plasticizers, specifically, BPA and four phthalates, the primary metabolites of DEHP, mono-2-ethylhexyl phthalate (MEHP) and three of its secondary metabolites, mono-(2-ethyl-5-oxohexyl) phthalate (5-oxo MEHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (5-OH MEHP) and mono-. The other seven pathways were derived from metabolites present in the metabolome There was one steroid, cortisol; two bile acids, glycocholic acid (GCA) and glycodeoxycholic acid, (GCDA); two vitamin E metabolites -6-hydroxychroman (α-CEHC) and 7,8-Trimethyl-2-(beta-carboxyethyl)-6-hydroxychroman (γ- CEHC): a food additive (Naringenin) and plant compound . Salicylate occurs widely in fruits and vegetables as well as aspirinThe study was approved by the Institutional Review Boards of Rutgers University-New Jersey Medical School, Rowan University-School of Osteopathic Medicine and their predecessor, the University of Medicine and Dentistry of New Jersey (UMDNJ). Subjects were recruited from the Pediatric Neurology and Pediatrics clinical practices of Rutgers-New Jersey Medical School. Written informed consent was obtained from each child’s guardian or parent as appropriate by either an investigator (XM) or a team member. A member of the clinic staff witnessed the procedure.The study population consisted of three groups of children from 3–16 years old. All the children who were diagnosed with ASD were under the care of a pediatric neurologist (X.M), and the diagnoses were made according to the Diagnostic Statistical Manual IV-TR and/or V, and were further confirmed by Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Scale-Generic, or both rating scales. Medical histories and comorbidity data were collected for the ASD and ADHD subjects. The diagnosis of ADHD was also made according to DSM-IV-TR criteria and DSM V and confirmed with the Vanderbilt ADHD diagnostic scale. Although DSM V was released in 2013, the DSM-IV-TR criteria for ASD and ADHD continued to be used to ensure that the diagnostic criteria would be the same as those employed in our earlier BPA and DEHP studies , 39, 40.Because medical and psychiatric comorbidities along with intellectual impairment are frequent among ASD and ADHD children , we incl2<0.95.Analytical methodology. The urinary creatinine concentrations were measured by us using a kit , and by LC-MSMS by Metabolon as part of the Metabolome. Metabolite concentrations measured by us were normalized to the Sigma-Aldrich kit values and the metabolome determined metabolites to the metabolome creatinine values. The reason for using metabolome creatinine values for metabolome metabolites was that the metabolome values correct for the possibility of any evaporative losses during sample preparation  for Metabolon. The correlation by regression analysis between the two methods was rThe analytical methodology for measuring BPA and Phthalates in urine has been previously described , 40. BriUrine specimens were sent to Metabolon, Inc.  for metabolomic analysis. The results were used for two purposes. Firstly, for calculating the efficiency of glucuronidation (= % bound) for certain compounds in the metabolome and secondly for analysis of the distribution of compounds in the metabolome.The requirements for being able to calculate a value for % bound from the metabolome is the presence of both free and bound compounds in the metabolome, total absence of bound compound after treatment of the urine with glucuronidase and a corresponding increase in the amount of free compound in the metabolome. The metabolomic screen gives values for the area-under-the -curve data (AUC) for the free and bound compounds. The measured area is a function of the amount of compound present, the fragmentation pattern and the mass spectrometer settings. The AUC cannot be used directly for calculating the efficiency of glucuronidation (% bound) because the fragmentation patterns for the free and bound compound are different. To calculate the total amount of a compound present and bound, it is necessary to first convert glucuronide areas into their free compound equivalents. The necessary conversion factors were determined by selecting 20 random urine specimens, preparing two aliquots, ‘control’ and ‘treated’. A fresh solution containing ~250–300 units/ml of E. Coli β-glucuronidase in 1M Ammonium Acetate was prepared immediately before use. Either 1 ml of enzyme solution (treated) or 1 ml of 1M ammonium acetate (control) was added to the control and treated tubes respectively. The tubes were incubated at 37°overnight (~19 hours) after which an aliquot was removed, frozen and sent to Metabolon, Inc.,  for metabolomic analysis.The amount of glucuronide hydrolyzed to free compound was obtained from the treated metabolome and a conversion factor for glucuronide to free compound equivalents. The control urine gives the AUCs for free (F) and, separately, glucuronidated (G) compound in the urine. The enzyme treated group gave a value for the total amount of compound T, (F + G) present in the original sample. The glucuronidated area was converted to free compound area equivalents by multiplying it by the conversion factor (R). The % free metabolite was calculated using the equation: % free = 100*(F/(T*R)). The % bound (efficiency of glucuronidation) values for the 12 glucuronidation metabolites were then calculated by subtracting the respective % free metabolite value from 100.Statistical Analyses. In calculating the frequency distributions for the % bound values, two subjects were found to have negative % bound values for BPA suggesting one of the two parameters (F or G) used for the calculation was in error. Because negative percentages for % bound metabolites are impossible, the data for the two children with negative percentages were excluded from further analyses involving BPA calculations but were included in analyses involving the other 11 glucuronides. In addition, compounds were excluded if the number of detections for a particular metabolite was < 50% of the sum of the total number of metabolites found in the ASD, CTR, or ADHD groups. No missing data were imputed.s), instead of Pearson product-moment correlations (rp) to estimate the magnitudes of the relationships of the BPA indices with respect to other types of metabolites. The use of rs is a valid approach to for estimating the magnitudes of association when data are minimally to moderately skewed and there are only several extreme outliers [The metabolome metabolites were normalized child’s urinary creatinine concentration , 43. An outliers . The staThe raw data, from which all that follows has been derived is given in the Twelve sets of % glucuronidation values were available for data analyses. Five were plasticizers, BPA the primary metabolite of DEHP, MEHP plus three of its secondary metabolites, 5 OXO-MEHP, 5 CX-MEPP and 5 OH-MEHP. MEHP is a more potent endocrine disruptor than the parent compound or its metabolites , 47. NonThe other seven glucuronidation efficiencies were derived from metabolites present in the metabolome. The criteria for being useable were (i) both the compound (free) and its glucuronide (bound) were present in the metabolome, (ii) after treatment with glucuronidase no glucuronide was present in metabolome, (iii) there was a parallel increase in the amount of free compound present, (iv) % bound values (= efficiency of glucuronidation) had to be obtained for at least half the subjects and (iv) the glucuronidation efficiencies covered a reasonable range .Seven metabolome compounds met all of these criteria. There was one steroid (cortisol), two bile acids (glycocholic acid (GCA) and glycodeoxycholic acid, (GCDA)), two vitamin E metabolites -6-hydroxychroman (α-CEHC) and 7,8-Trimethyl-2-(beta-carboxyethyl)-6-hydroxychroman, (γ- CEHC)), and two plant compounds, the fruit flavonoid Naringenin and Salicylate. Salicylate occurs widely in fruits and vegetables as well as the drug aspirin. When drugs are the source, Salicylate level in the urine are orders of magnitude greater than the background level. There was one such instance in the present study; a subject was found to have a level approximately six times higher than the total sample’s Salicylate mean. This data point was dropped from all further analyses.The quantification of the data and subsequent statistical analyses were conducted in stages. First, the skewness indices of the frequency distributions for the 12 glucuronidation efficiencies (% bound) were calculated. There were five metabolites with negative skewness indices < -1.0 and one metabolite with a positive skewness > 1.0. Therefore, we used nonparametric statistical analyses for comparisons between the 12 glucuronidation efficiency data sets.To determine whether there was sufficient variability within each of the 12 glucuronidation pathways to support further analyses, the frequency distribution for each of the 12 metabolites was divided into tertiles, highest, middle and lowest. The means and standard deviations shown in Metabolon reports the metabolome results with each detected compound categorized into super-families and sub-pathways using Metabolon’s classification scheme. There were 8 super-pathways, Amino Acids (n = 226), Carbohydrates (n = 29), Cofactors and Vitamins (n = 23), Energy (n = 14), Lipids (n = 124), Nucleotides (n = 53), Peptides (n = 27), Xenobiotics (142) and unclassified compounds (n = 44). The super-pathways are broken up into numerous sub-pathways. Examples from the Amino Acids super-pathway are Glutamate metabolism, Histidine metabolism. Leucine, Isoleucine and Valine Metabolism etc. The Xenobiotics super-family include Drugs, Food additives, Chemicals etc. The two largest super-pathways were Amino Acids and Xenobiotics.Analyses of mean fold differences between the means of the metabolites in the metabolome were used to confirm that the three groups of children were metabolically distinct and that correlational analyses to confirm associations among the plasticizers and clinical state were statistically warranted. To demonstrate that the three groups of children were metabolically different, the mean fold ratios of the means for the ASD, CTR, and ADHD groups were calculated for each of the metabolites in the metabolome . The staExamination of the 151 metabolites in the Xenobiotic super-family showed that this was attributable to highly skewed data for two drugs, Acetaminophen and Ibuprofen, and the artificial sweeteners, Sucralose, Acesulfame K and Saccharine and their metabolites. This observation is not surprising because these drugs are common analgesics, and the three sweeteners are frequently added to low calorie drinks. Using z score analyses, number of SDs above the cluster’s mean, to identify outliers for these five compounds, we found z scores > 10 and even > 25. Eliminating these five compounds and their metabolites from the Xenobiotics cluster reduced the N from 151 to 142 for Xenobiotics and brought the SDs into the same range as the other compound families in the Metabolome. Removing them from the total metabolome (n = 701→n = 692) had only minimal effect on the statistical analyses because of the large number of compounds in the metabolome. A few steroid values appeared to be appeared to be anomalously high (<2SD from mean) across all groups. The likely cause was androgens in the steroid cluster (7 out of 22 compounds); there is a higher percentage of boys in the ASD and ADHD groups, hence more androgens .The frequency distributions of mean fold differences were then graphically examined and approached those that might be expected for a normal distribution, and independent t tests were considered appropriate for estimating the significance of mean differences among the groups. However, some of the significant mean ratio differences between groups disappeared after application of a Bonferroni correction.Some differences remained. For the total Metabolome, with or without inclusion of analgesics and soda sweeteners, the mean fold ratios were statistically significant for the ASD/CTR and ADHD/CTR mean ratios indicating that both the ASD and ADHD mean fold ratios were different from each other and the controls. There also were significant differences for the amino acids and vitamins-co-factors super-pathways. Mean fold ratios for the ASD and ADHD groups differed significantly from the CTR group, but not from each other. For the purposes of this study, the need was to establish that the three groups of children were metabolically distinct, and the mean fold ratio data that are displayed in ss) among the 12% bound glucuronidation pathways. As expected for closely related metabolic groups, there are multiple significant correlations. Although the pattern of correlations is complex, it is not random. The phthalates are chemically related, and their rss tend to cluster together. The two bile acids correlate only with each other. Cortisol seems to be unique as does Naringenin. Details about the relationships among putative groupings of the 12 glucuronidation efficiencies are tangential for the present study; the requirement for the present study is that the 12 metabolites’ rss display different patterns of significant relationships indicating that although the pathways are related, they do differ  and 15% for the ADHD (p<0.001) groups. MEHP showed similar, but non-significant trends; ASD 57.6 ± 4.3%, CTR 69.2 ± 3.4% and ADHD 61.7 ± 4.3%) corresponding to reductions of the efficiency of glucuronidation of 17% for ASD and 11% for ADHD.ss were calculated to determine what the relationships of the % bound values were for each of the 12 pathways with each of the 692 metabolites in the metabolome. Only the number of positive and negative rss that were significant at ≥ 0.05 level, two-tailed test, were included in the subsequent analyses. No attempt was made to control for the plausibly high false positive rate that would occur from calculating 478,864 (3 x 12 x 692) rss. Our focus was on identifying chemically recognizable patterns of relationships. Accordingly, we examined the results by familial groupings.The next step in the statistical analyses was to examine the relationships between the metabolome metabolites and the 12 pathways with each clinical state . First the rp ≤ .05) correlations. The sum of the % positive and % negative correlations equals 100%. The situation changes when the distributions of positive and negative correlations are examined as percentages of the total number of significant  and the control group the least, 13 for CTR and ASD, 4 for CTR vs ADHD. Of the 23 in common between ASD and ADHD, the majority were with the amino acids grouping . This isThere is an extensive body of epidemiological evidence for a relationship between neurodevelopmental disorders and environmental pollutants such as plasticizers , 38. BeyIn the present study, two semi-independent approaches were used for analyzing the data to demonstrate a linkage between plasticizer metabolism and neurodevelopmental disorders. We use the term semi-independent rather than independent because both approaches share a dependence on the value for glucuronidation efficiency for the 12 pathways. Approach #1 uses only the glucuronidation efficiency data; approach #2 uses the interaction between the 12 pathways and the metabolome. Genetic factors are likely to be important factors in determining the efficiency of glucuronidation.Like the BPA pathway, the MEHP pathway, the primary and most abundant metabolite of the actual plasticizer DEHP, showed a non-significant trend towards an association with both ASD and ADHD. None of the other glucuronidation pathways showed any signs of a relationship to either ASD or ADHD. BPA and DEHP are chemically similar so some overlap in the efficiency of glucuronidation pathways, even if not always statistically significant is not surprising. Approach #1 shows that there is a unique association between compromised detoxification of the plasticizers, BPA and MEHP to neurodevelopmental disorders. Approach #2 confirms this finding.ss in the ASD and ADHD groups were positive for the BPA and MEHP pathways.The figures present a second, visual approach for understanding a wealth of metabolomic data addressing the same. This time instead of only comparing the 12 glucuronidation pathways against each other for evidence of a relationship to disorders, we examined the relationship of the 12 glucuronidation pathways to the metabolome and whether those relationships were associated with clinical diagnosis. Again, interaction between ASD and ADHD parallel each other and show a pattern not shown by any of the other pathways. Specifically, 90+% of the significant pThe metabolome reflects metabolism within the body. Its relationship to plasticizer exposure should be the same for all three groups of children. For BPA the difference from the control children is particularly striking; the correlations for control children go in the opposite direction than the children with neurodevelopmental disorder. For MEHP the figure differs in one important way, this time nearly all the control correlations go in the same direction as ASD and ADHD. For ADHD and ASD there is an association between disease and compromised plasticizer detoxification. Based on what is known about the etiology of the two neuro-developmental disorders, the simplest and most direct pathway for BPA and DEHP to lead to neurodevelopmental disorder is the scheme outlined in It is generally accepted that the etiology of ASD is multifactorial and the most likely mechanism involves environmental toxicants acting upon genetically susceptible individuals –7. We arThis does occur in humans. A particularly relevant example is a study by Luo et on the relationship between UGT polymorphisms and environmental endocrine disruptors (including BPA) levels in Polycystic Ovary Syndrome . The glThe metabolic consequence of this greater residence time in the body of the active forms of the two plasticizers is more exposure to the tissues of the two plasticizers. Elevated blood levels of BPA and MEHP for children with ASD and ADHD have been reported , 52–55. This is not likely to be without consequence. Numerous studies have shown both function as endocrine disruptors –20. EndoAlmost certainly there are other pathways that lead to ASD and ADHD. The scheme presented in The strengths of this study are: (i) Provides biochemical data independent of epidemiology confirm prior epidemiological studies of associations between the common plasticizers and two common neurodevelopmental diseases. (ii) Fills a gap in present understanding by ‘identifying’ a plausible link (compromised glucuronidation) between genetics, plasticizer exposure and endocrine disruption. (iii) Supports this identification by two semi-independent methods. Firstly, by showing that compromised glucuronidation of BPA is associated with both ASD and ADHD. MEHP showed a non-significant trend. Secondly, documenting a unique relationship between glucuronidation efficiency and the metabolome. Finally, there is nothing new about the efficiency of glucuronidation affecting metabolism and being associated with disease, what is new is that we show this is also a plausible mechanism for ASD and ADHD.Children with ASD and ADHD are clinically and biologically diverse. The diagnosis for this study was made on the basis of behavioral criteria. Medication intake could potentially impact upon the regulation of the glucuronidation pathway(s). Despite this confounder, ASD and ADHD groups had significantly reduced capacity in glucuronidation of BPA, which reinforces the salient results present here. Future studies with stricter inclusion criteria are required to confirm the present findings.The principal measured parameter in this study was the % of the total compound that is glucuronidated at the time the urine specimen is collected. We equate this as also being a measure of the efficiency of glucuronidation over time from the urine collection for analysis and the immediate prior specimen.The number of subjects is relatively small and measured at only one point in time. However it was adequate to obtain interpretable statistically significant data.The glucuronidation pathway is the major route for xenobiotic elimination, whereas sulfation is a minor pathway accounting for <10% –25. We hWe have no reliable measurement of exposure to plasticizers. However, there is no reason to suspect any major differences between the three groups of children. The children were all drawn from a common environment, the 24 hr. dietary recall data did not reveal any gross differences and the effect sizes in this study were large. Plots of urine total plasticizer amounts against the efficiency of glucuronidation showed no correlations.It is possible that the causative agents are not BPA and MEHP. Rather BPA and MEHP serve as markers for other unknown compound(s) that are metabolized by the same BPA and MEHP pathways.The two major neurodevelopmental disorders in children are ASD and ADHD. The two disorders are clinically and metabolically different from control children but share an association with compromised detoxification pathways for the plasticizers, BPA and MEHP.S1 Dataset(XLSX)Click here for additional data file.


Knowledge of risk factors for attention-deficit/hyperactivity disorder (ADHD) may facilitate early diagnosis; however, studies examining a broad range of potential risk factors for ADHD in adults are limited. This study aimed to identify risk factors associated with newly diagnosed ADHD among adults in the United States (US).Eligible adults from the IQVIA PharMetrics® Plus database (10/01/2015-09/30/2021) were classified into the ADHD cohort if they had ≥ 2 ADHD diagnoses (index date: first ADHD diagnosis) and into the non-ADHD cohort if they had no observed ADHD diagnosis (index date: random date) with a 1:3 case-to-control ratio. Risk factors for newly diagnosed ADHD were assessed during the 12-month baseline period; logistic regression with stepwise variable selection was used to assess statistically significant association. The combined impact of selected risk factors was explored using common patient profiles.A total of 337,034 patients were included in the ADHD cohort  and 1,011,102 in the non-ADHD cohort . During the baseline period, the most frequent mental health comorbidities in the ADHD and non-ADHD cohorts were anxiety disorders (34.4% and 11.1%) and depressive disorders (27.9% and 7.8%). Accordingly, a higher proportion of patients in the ADHD cohort received antianxiety agents (20.6% and 8.3%) and antidepressants (40.9% and 15.8%). Key risk factors associated with a significantly increased probability of ADHD included the number of mental health comorbidities , along with certain mental health comorbidities , use of antidepressants (OR: 1.87) and antianxiety agents (OR: 1.40), and having ≥ 1 psychotherapy visit (OR: 1.70), ≥ 1 specialist visit (OR: 1.30), and ≥ 10 outpatient visits (OR: 1.51) . The predicted risk of ADHD for patients with treated anxiety and depressive disorders was 81.9%.Mental health comorbidities and related treatments are significantly associated with newly diagnosed ADHD in US adults. Screening for patients with risk factors for ADHD may allow early diagnosis and appropriate management. Attention-deficit/hyperactivity disorder (ADHD) is a debilitating neurodevelopmental condition with an estimated prevalence of 4.4% among adults in the United States (US) . ADHD isThe diagnostic challenges of ADHD are partially attributable to the frequent comorbid mental disorders , 6. CertADHD is associated with a wide range of psychosocial, functional, and occupational problems in adults . A delayKnowledge on patient characteristics associated with a higher risk of ADHD in adults and the patient journey prior to a clinical ADHD diagnosis may facilitate early diagnosis and the provision of appropriate management. The current study was conducted to identify risk factors for newly diagnosed ADHD in adult patients using a large claims database in the US. The potential utility of the results was also demonstrated through exploring the combined impact of selected risk factors on ADHD risk prediction using fictitious common patient profiles.Data from the IQVIA PharMetrics® Plus (IQVIA) database covering the period of October 1, 2015, to September 30, 2021, were used. The IQVIA database contains integrated claims data of over 190 million beneficiaries across the US and includes information on inpatient and outpatient diagnoses and procedures, prescription fills, patients’ pharmacy and medical benefits, inpatient stays, and provider details. Additional data elements encompass dates of service, demographic variables, plan type, payer type, and start and stop dates of health plan enrollment. Data are de-identified and comply with the patient requirements of the Health Insurance Portability and Accountability Act (HIPAA); therefore, no review by an institutional review board nor informed consent was required per Title 45 of CFR, Part 46.101(b)(4) .ADHD cohort comprised patients with ≥ 2 ADHD diagnoses recorded on a medical claim on distinct dates at any time during their continuous health plan enrollment; and the non-ADHD cohort comprised patients without any ADHD diagnoses recorded on a medical claim at any time during their continuous health plan enrollment. To account for large differences in sample size and to retain statistical power, a 1:3 case-to-control ratio was used. Specifically, eligible patients were randomly selected into the non-ADHD cohort such that the total number of patients in the non-ADHD cohort was three times that of the ADHD cohort.A retrospective case-control study design was used. Eligible adults were classified into two cohorts based on the presence of ADHD diagnoses : the The index date was defined as the first observed ADHD diagnosis among the ADHD cohort and a randomly selected date among the non-ADHD cohort. To allow sufficient time to capture potential risk factors for ADHD, patients were required to have ≥ 12 months of continuous health plan enrollment prior to the index date. The baseline period was defined as the 12 months pre-index.Patient characteristics and potential risk factors for newly diagnosed ADHD were assessed during the baseline period for each cohort, separately. Potential risk factors considered in this study were identified through a targeted literature review and observable variables in the data and included demographic characteristics , clinical characteristics , pharmacological treatments , healthcare resource utilization . Risk factors for ADHD in this study were identified from potential risk factors that had statistically significant association with newly diagnosed ADHD, as described in the next section.Descriptive statistics were used to summarize baseline patient characteristics and potential risk factors for newly diagnosed ADHD. Means, medians, and standard deviations (SDs) were reported for continuous variables; frequency counts and percentages were reported for categorical variables.Univariate statistics were used to compare potential risk factors between the ADHD and non-ADHD cohorts. The magnitude of the difference between cohorts was assessed by calculating the standardized differences (std. diff.) for both continuous and categorical variables.Logistic regression model with stepwise variable selection was used to assess statistically significant association between potential risk factors and ADHD diagnosis. Potential risk factors were eligible for inclusion in the logistic regression based on their univariate association with ADHD diagnosis . Potential risk factors presented in < 0.5% of the sample were discarded. Variables included in the last iteration of the stepwise selection process were considered as risk factors of the study outcome. The association between risk factors and ADHD diagnosis were reported as odds ratios (ORs) along with their 95% confidence intervals (CIs) and p-values.To facilitate the interpretation of the regression analyses, the predicted risk of ADHD based on regression coefficient estimates was evaluated for six fictitious common patient profiles corresponding to patients who harbor selected combinations of ADHD risk factors. This exploratory analysis allowed for the estimation of how the risk of having ADHD would vary had the same person had additional risk factors but otherwise the same characteristics.The total sample comprised 1,348,136 patients, including 337,034 in the ADHD cohort and 1,011,102 in the non-ADHD cohort . In both cohorts, slightly over half of the patients were female , and the South was the most represented region .During the baseline period, the most frequent physical comorbidities in the ADHD and non-ADHD cohorts were hypertension , obesity , and chronic pulmonary disease .A lower proportion of patients had no mental health comorbidities in the ADHD cohort than the non-ADHD cohort . The mean ± SD number of mental health comorbidities was 1.2 ± 1.4 in the ADHD cohort and 0.5 ± 0.9 in the non-ADHD cohort (std. diff. = 0.65). The most frequent mental health comorbidities in the ADHD and non-ADHD cohorts were anxiety disorders , depressive disorders , sleep-wake disorders , trauma- and stressor-related disorders , and substance-related and addictive disorders .A higher proportion of patients in the ADHD than the non-ADHD cohort received antidepressants , antianxiety agents , anticonvulsants , and antipsychotics .The ADHD cohort, relative to the non-ADHD cohort, had generally higher mean ± SD rates of healthcare resource utilization, including more psychotherapy visits , emergency room visits , outpatient visits , and specialist visits ; the number of inpatient visits were similar between cohorts .The risk factors with a significant association with an ADHD diagnosis are presented in Fig. Other key risk factors associated with a significantly increased probability of having an ADHD diagnosis included the number of mental health comorbidities ; certain mental health comorbidities, including feeding and eating disorders (OR: 1.88), bipolar disorders (OR: 1.50), depressive disorders (OR: 1.37), trauma- and stressor-related disorders (OR: 1.27), anxiety disorders (OR: 1.24), sleep-wake disorders (OR: 1.23), and obsessive compulsive disorders (OR: 1.22); use of antidepressants (OR: 1.87) and antianxiety agents (OR: 1.40); and having ≥ 1 psychotherapy visit (OR: 1.70), ≥ 1 specialist visit (OR: 1.30), and ≥ 10 outpatient visits (OR: 1.51) .Selected risk factors identified from the logistic regression analyses were used to create fictitious common patient profiles to demonstrate their combined impact on the predicted risk of having an ADHD diagnosis Fig. . Five ofBased on these patient profiles, the predicted risk of ADHD was the highest among patients with treated anxiety and depressive disorders (profile 3). More specifically, a patient presenting with the characteristics described in this profile would have an 81.9% likelihood of being diagnosed with ADHD in the coming year. The profile with the next highest predicted risk of ADHD was patients with treated depressive disorder , followed by patients with treated anxiety disorder . Profiles corresponding to a moderate predicted risk of ADHD included patients with untreated anxiety and depressive disorders  and patients with sleep-wake disorder . The predicted risk for ADHD among low-risk patients (profile 6) was 1.5%.This large retrospective case-control study has identified a broad range of risk factors associated with ADHD in adults and quantified the added likelihood of an ADHD diagnosis contributed by each factor. Certain mental health comorbidities and their associated treatments and care were found to be significantly associated with newly diagnosed ADHD in adults. Specifically, the presence of common mental health comorbidities of ADHD such as anxiety and depressive disorders was associated with 24% and 37% increased risk of having an ADHD diagnosis, respectively. The use of pharmacological treatments for these conditions such as antianxiety agents and antidepressants was associated with an increased risk of having an ADHD diagnosis of 40% and 87%, respectively; having at least one prior psychotherapy visit was also associated with a 70% increased risk. Demographically, being younger and living in the South were found to be risk factors for having an ADHD diagnosis. The combined impact of selected risk factors on the predicted ADHD risk was explored through specific patient profiles, which demonstrated how the findings may be interpreted in clinical settings. The presence of a combination of risk factors may suggest that a patient is at a high risk of having undiagnosed ADHD and signify the need for further assessments. Collectively, findings of this study have extended our understanding on the patient path to ADHD diagnosis as well as the characteristics and clinical events that could suggest undiagnosed ADHD in adults.Most prior studies examining characteristics associated with ADHD have focused on a single or a few factors, and many were conducted in pediatric populations –23. NoneThe current study also found that an overall higher healthcare resource utilization was a characteristic associated with newly diagnosed ADHD among adult patients. A potential interpretation of this finding is that an individual who experienced ADHD-related symptoms might visit a psychologist or physician frequently to seek help for the symptoms; thus, a high level of prior healthcare resource utilization may be a sign that an individual could have undiagnosed ADHD. Clinical judgement should be applied to determine whether further evaluation for ADHD is needed on a case-by-case basis considering the presence of other high-risk characteristics.The diagnosis of ADHD can be challenging, particularly among adults , 3. The It is worth mentioning that causes for ADHD is multifactorial, and multiple risk factors may contribute to the risk of having ADHD . Some riThe findings of the current study should be considered in light of certain limitations inherent to retrospective databases using claims data, including the risk of data omissions, coding errors, and the presence of rule-out diagnosis. Nonetheless, while few studies specifically assessed the validity of ICD-10-CM codes for ADHD diagnoses in claims data, literature evidence has suggested high accuracy of ICD-9-CM codes in identifying neurodevelopmental disorders, including ADHD, and a good correspondence between the ICD-9 and − 10 codes is expected , 30. FurThis large retrospective case-control study found that mental health comorbidities and related treatments and care are significantly associated with newly diagnosed ADHD in US adults. The presence of a combination of risk factors may suggest that a patient is at a high risk of having undiagnosed ADHD. The results of this study provide insights on the path to ADHD diagnosis and may aid clinicians identify at-risk patients for screening, which may facilitate early diagnosis and appropriate management of ADHD.


To raise awareness of attention-deficit/hyperactivity disorder (ADHD) as an underdiagnosed, undertreated disorder in adult patients with comorbid substance use disorder (SUD) who are misdiagnosed with other common psychiatric illnesses and to reduce fear and hesitancy in prescribing stimulants as treatment in such a patient population. ADHD diagnosis is easier in the child and adolescent population than the adults due to comorbidities of other psychiatric illnesses and SUD. However, diagnosing ADHD appropriately in an increasing number of adult patients presents challenges. Even if they get diagnosed appropriately, the stigma of substance use disorder holds the providers prescribing stimulant medications for such patient populations due to the high comorbidity of ADHD with SUD. Accurate diagnosis of ADHD in adults is a worthwhile endeavor as this diagnosis is comorbidly present in many mood and substance use disorders patients. Treating ADHD in this population can improve clinical symptoms and overall quality of life. Adults with substance use disorders (SUD) and attention-deficit/hyperactivity disorder (ADHD) are increasingly presenting in clinical practice . It carrMr. X was self-referred to our outpatient clinic for medication-assisted treatment for opioid use disorder and adjustment of his psychotropic medications regimen. He is a homeless 28-year-old Caucasian male living in a shelter and receiving support from the Department of Social Services (DSS). This patient had been previously diagnosed with bipolar disorder, high-functioning autism spectrum disorder, post-traumatic stress disorder (PTSD), and opioid use disorder (on Suboxone® R).At intake, he endorsed craving opioids and compulsive behavior to obtain opioids. Mr. X's daily medications included haloperidol 10 mg twice daily, bupropion 300 mg extended-release (XL) daily, and divalproex sodium delayed-release (DR) 500 mg in the morning and 1000 mg at bedtime for mood swings. He noted none or minimal improvement in these symptoms despite medication compliance for more than three months. The divalproex sodium level was obtained and within the therapeutic range. All other routine labs were normal. During history taking, he verbalized subsyndromal mood swings, including feeling sad with low self-esteem and low levels of energy and motivation most of the time. He stated that medications, including Suboxone®, helped his cravings and compulsive opioid-seeking behavior but did not help control his mood swings despite of using Suboxone® for more than six months. Though Mr. X carried the diagnosis of high-functioning autism spectrum disorder, he did not have behavioral problems and denied any anxiety disorder symptoms related to the same.He had two prior psychiatric hospitalizations in the context of worsening depression and elevated mood. His hospitalization had resulted from an inability to focus, concentrate and live his life independently, which worsened his mood symptoms. Various serotonin reuptake inhibitors (SSRI) and serotonin and norepinephrine reuptake inhibitors (SNRI) taken with the above medications did not significantly improve. He lost his job due to his inability to focus and concentrate. As a result, he could not financially support himself and was kicked out by his family members. He had a history of smoking marijuana, around four to five puffs daily, and was attending contingency management group therapy to relieve his potential symptoms of underlying ADHD and mood disorder.Aftercare was established, and Mr. X's symptoms were monitored for about three months. He was initially tried on oxcarbazepine for two months due to minimal clinical response. A decision to taper all his psychotropic medications  was made. His symptoms showed no improvement, and the patient was monitored for potential worsening psychosis and mood disorder. Eventually, a decision was made to start Vyvanse® (lisdexamfetamine) 20 mg daily, empirically for diagnosis and to relieve his symptoms as the patient was resistant to traditional psychotropic medications and was manifesting subsyndromal mood swings, including low self-esteem, low level-of energy, motivation and poor impulse control. He was followed for four week on Vyvanse® 20 mg. After starting Vyvanse®, Mr. X's mood improved, resulting in him seeking an employment. He verbalized minimal mood swings. Slowly, the Vyvanse® dose was titrated to 40 mg daily, which further improved the remainder of Mr. X's symptoms. He started doing better emotionally and showed improvement in his interpersonal, functional, and occupational life. Based on his history, clinical presentation, nonresponse to mood-stabilizing drugs, and improvement of symptoms on Vyvanse®, a diagnosis of ADHD was made, and the prior diagnosis of bipolar disorder was removed. He was continued on Vyvanse® and Suboxone® for ADHD and Opioid use disorder.Adults with concomitant SUD and ADHD are a common presentation in clinical practice. Research shows a connection between the two conditions, with over 15% of young adults with ADHD having comorbid SUD . ADHD isAspects that caused a misdiagnosis in Mr. X’s case include changes in symptoms due to age and overlapping symptoms with other comorbidities. As such, patients with ADHD are often underdiagnosed and undertreated . The higConsidering Mr. X’s case, one must also consider the role of a hedonic tone in his clinical presentation. Sternat and Katzman have argued that low hedonic tone is a common denominator in ADHD, SUD, and depression and that the primary neural pathway implicated in emotional affect is the limbic-cortical-striatal-pallidal-thalamic circuits. Dysfunction in various components of this circuitry has also been implicated in developing the aforementioned related psychopathologies and the potential for poor response to selective serotonin reuptake inhibitors (SSRIs) . Our indStigma has been explored extensively in the context of psychiatric disorders. Stigma is a crucial determinant in help-seeking, treatment outcomes, and quality of life of individuals with psychiatric conditions . Due to The disorder is linked with a 30-140% increase in morbidity and 85-110% in mortality rates . Adults In the presented case, Mr. X had been diagnosed with opioid use disorder and had two previous hospitalizations due to depression and elevated mood. He also had a history of smoking marijuana. Substance use often mimics ADHD symptoms. For example, uncontrolled marijuana use is linked with impairments in cognitive functioning, memory, and attention . Mr. X pFor each specific case, the guidelines for ADHD management and treatment may vary. The context in which the treatment is being administered and the country’s guidelines are crucial aspects to consider in ADHD management. The International Collaboration of ADHD and Substance Use (ICASA) recommends integrating SUD and ADHD treatment early enough. The first line of treatment includes the use of psychostimulants, mostly methylphenidate and amphetamine formulations ,18. RespADHD, SUD, and other common psychiatric illnesses occur comorbidly in adults. Symptoms of ADHD closely resemble mood disorders, and in a patient with concomitant substance use disorder, an accurate diagnosis of ADHD becomes a challenge. Physicians often hesitate to prescribe stimulants in such illicit drug-use patient populations as stimulants carry the risks of addiction and abuse. Though the clinical picture of our patient was unclear, starting and monitoring stimulants diagnostically changed this patient's life. This case represents another example where prescribing necessary medications  without any delay improved clinical symptoms and the quality of life of a patient. It provides another example where hesitancy in prescribing appropriate medications should be challenged. Understanding different ADHD and mechanisms of action of medication options allows clinicians to customize treatment to each patient's needs and attain better outcomes. Ultimately, the quality of life of a person with comorbid ADHD and SUD improves with the proper diagnosis and treatment.


To analyze the impact of yoga and music intervention on child consumers, we selected 60 eligible child consumers from yoga and music companies.This preliminary study used a randomized controlled design to investigate whether a 16-week combined yoga and music intervention improves attention, hyperactivity/impulsivity, and oppositional defiant disorder (ODD) in 60 children with attention-deficit/hyperactivity disorder (ADHD) comorbid with ODD aged 4–6 years. It also preliminarily identified which intervention is best for these children among three types: combined yoga and music, yoga-only, and musiconly interventions. We used both the parent- and teacher-rated MTA SNAP-IV ADHD Rating Scale for data collection.We found that the combined yoga and music intervention had a positive effect on inattention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD. The combined yoga and music intervention was the most effective in reducing inattention , hyperactivity/impulsivity , and ODD behaviors  in children with comorbid ADHD and ODD. Thus, the combined yoga and music intervention was the most effective and had a more comprehensive effect on children with combined ADHD and ODD compared with the two other interventions .Our findings provide preliminary evidence for the use of combined yoga and music interventions on a daily basis as a safe and effective adjunctive treatment for children with comorbid ADHD and ODD. The children in the article refer to child consumers of yoga and music companies. The largest advantage of pharmacological therapy is its fast effects , but theAcademicians have indicated that a lack of exercise leads to delayed prefrontal brain development . Mehren In recent years, yoga, which is a form of exercise activity, has been introduced as a potential treatment for children with ADHD . RegardiSecond, yoga may improve hyperactivity/impulsivity in children with ADHD, and related interventions are described as being promising for tackling behaviors and emotions of children with ADHD . FurtherRegarding music therapy, the literature about its effects on the symptoms of children with ADHD is rather small. Among the few researchers that have explored the topic, some describe that the effects are long-lasting and others that these effects are short-lasting and warrant more in-depth study. Particularly, Thus, to address the significant aforementioned gaps in the current literature, this study aimed to investigate whether a combined yoga and music intervention improves attention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD. It also attempted to identify which intervention is best for children with comorbid ADHD and ODD among three intervention types: combined yoga and music, yoga-only, and music-only interventions. The three research questions of this study were as follows: first, does the combined yoga and music intervention improve symptoms in children with comorbid ADHD and ODD and is it a safe and effective daily adjunctive treatment modality for these children? Second, to what extent do the three interventions  affect attention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD, and which intervention has the most effective treatment effect? Third, what are the possible mechanisms of action of the combined yoga and music intervention for children with comorbid ADHD and ODD?We used both the parent- and teacher-rated MTA SNAP-IV ADHD Rating Scale to rate children with comorbid ADHD and ODD aged 4–6 years before and after the interventions for inattention, hyperactivity/impulsivity, and ODD. Based on the existing, albeit limited, research, we formed two hypotheses:Hypothesis 1 (H1): The existence of significant differences in the combined intervention of yoga and music on attention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD will be determined.Hypothesis 2 (H2): The existence of significant differences in attention, hyperactivity/impulsivity, and ODD between the yoga and music, yoga-only, music-only, and control groups in children with comorbid ADHD and ODD will be determined.We describe the four major study contributions herein. First, this study fills a research gap regarding the lack of data on the effects of combined yoga and music interventions on children with comorbid ADHD and ODD in China; it also makes a theoretical and practical contribution by expanding the evidence on treatment options for children with comorbid ADHD and ODD. Second, we examined the effects of a combined yoga and music intervention on ODD, implying that the current study was more comprehensive than previous studies that focused solely on core symptoms of ADHD, such as attention deficit and hyperactivity/impulsivity. Third, we compared the effects of three types of intervention  on the aforementioned variables in children with comorbid ADHD and ODD, thus providing valuable reference data for the implementation of complementary treatments that can be used daily. Fourth, we preliminarily studied the mechanisms of the impact of combined yoga and music interventions in children with comorbid ADHD and ODD, expanding existing theories and making significant theoretical and practical contributions to the implementation of yoga and music interventions in this population.2.2.1.This interventional study used a randomized controlled design because randomized controlled trials follow the principles of randomization and control, making them the gold standard for producing reliable clinical evidence .p > 0.05). Regarding the posttest measure of the last intervention session of this study for each group that received an intervention, it was conducted 24 h after the last scheduled intervention ; the 15 participants in the control group did not receive any intervention; the 15 children in the yoga-only group received a 16-week yoga intervention ; the 15 participants in the music-only group received a 16-week music intervention . Therefore, the power for this study was 0.8.We used the stratified whole-group random sampling method to divide children’s consumer of yoga company and the music company into three levels: small, middle, and large classes. Three classes were randomly selected from each level to form the sample of this study . ThroughAfter screening, 60 children aged 4–6 years were identified as eligible for inclusion, among which 18 were in the small class, 25 in the middle class, and 17 in the large class. All 60 children with comorbid ADHD and ODD  and their legal guardians that partook in this randomized controlled trial volunteered to participate, and all participants and their legal guardians provided written informed consent. All participants and their legal guardians were informed in detail about the design, purpose, and intent of this study, and all study procedures complied with the Declaration of Helsinki and adhered to the basic ethical principles of human research. Furthermore, this study was conducted in compliance with all principles of the Standards for Ethics in Sport and Exercise Science Research , and wasn = 15), control (n = 15), yoga-only (n = 15), and music-only groups (n = 15).Using computer assignment software, the 60 participants were randomly divided into a yoga and music .The yoga intervention  includesThe music intervention was performed by playing music that the children liked in a quiet classroom at the kindergarten of Chengdu Normal University, wherein participants chose their favorite track among some common children’s songs and listened to it for 10 min. Examples of the Chinese children’s songs used are “Little Star,” “Red Dragonfly,” “Jingle Bells,” and “Wahaha.” The rationale behind the choice of these tracks is described in the study by 2.5.p < 0.05.Data analysis was performed using SPSS, version 26.0. First, we checked the data for normality, variance chi-square, and the presence of outliers. We found no outliers, no variable showed a highly non-normal distribution, and based on a cut-off for the Shapiro–Wilk test of >0.05, all data were normally distributed . Second,3.p > 0.05).All 60 participating children lived in the Wenjiang District of Chengdu and were from families of middle socioeconomic status. For the total sample, the mean age, height, and weight was 4.98 years , 109.33 cm , and 20.9 kg , respectively. Regarding mean age and height, the group with the highest value was the control group . Regarding mean weight, the group with the highest value was the yoga group . p < 0. 05). These results indicate that our measure has good validity. This shows that the scale we used has good psychometric properties.In our study, the internal consistency reliability Cronbach’s alpha coefficient of the Chinese version of the MTA SNAP-IV ADHD Rating Scale was 0. 94, and the Cronbach’s alpha coefficients of the three subscales of inattention, hyperactivity/impulsivity, and ODD were 0.89, 0.88, and 0.87, indicating good reliability of our measure. We performed validated factor analysis of the Scale using Amos software, with RMSEA <0. 05, H1, as shown in p < 0.05). The results suggest that the combined yoga and music intervention can help children with comorbid ADHD and ODD focus their attention and reduce hyperactivity/impulsivity and ODD behaviors.Regarding H2, as shown in For p = 0.064), while the remaining groups showed significant differences between inattention, hyperactivity/impulsivity, and ODD (p < 0.05).To compare whether there were statistically significant differences between the yoga and music, yoga-only, music-only, and control groups, we conducted multiple comparisons of group data using one-way analysis of variance. The posttest data for inattention, hyperactivity/impulsivity, and ODD data in the four groups met the conditions of normality and Chi-squareness. The results of the posttest data showed that there were no significant differences between the yoga and music groups in terms of ODD , hyperactivity/impulsivity (followed by the yoga- and music-only interventions), and ODD behaviors, confirming H2. We also preliminarily verified that the music intervention enhanced the effect of the yoga intervention, and that the latter helped the former’s effect last longer.Our study investigated the effects of a combined yoga and music intervention on attention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD using both the parent- (pretest and posttest) and the teacher-rated (pretest and posttest) MTA SNAP-IV ADHD Rating Scales. We found that the combined yoga and music intervention had a positive effect on attention, hyperactivity/impulsivity, and ODD, supporting H1, while using the parent- and teacher-rated MTA SNAP-IV ADHD Rating Scales, implies that 16 weeks of a combined yoga and music intervention can positively significantly impact children with comorbid ADHD and ODD. Specifically, it can help them to focus their attention and reduce hyperactivity/impulsivity and ODD behaviors. Thus, this combined intervention may be used on a daily basis and as an optional, safe, effective adjunctive therapy for children with comorbid ADHD and ODD. Our study fills a gap in the literature regarding the lack of evidence on the effects of combined yoga and music interventions on ADHD symptoms in Chinese children. Additionally, although researchers thus far have mostly focused their examinations on the effects of yoga- and music-only interventions on attention and hyperactivity in children with ADHD, our study focused additionally on ODD and compared the effects of the combined yoga and music intervention with those of yoga- and music-only interventions—with the findings supporting H2. Thus, this study provides more comprehensive research data than previous studies and enriches the evidence on the effects of joint yoga and music interventions on children with comorbid ADHD and ODD, making both theoretical and practical contributions to the field.The confirmation of Our study shows the positive effect of yoga interventions on attention and hyperactivity/impulsivity in children with comorbid ADHD and ODD, and this is consistent with the evidence in prior research on children with ADHD . Yoga prConsidering the paucity of evidence on the effects of yoga interventions on ODD behaviors in children with ADHD, in addition to attention and hyperactivity/impulsivity, we examined the effects of a combined yoga and music intervention on ODD—and as aforementioned, the reduction in this variable was significant. Irritability and defiance are the two typical characteristics of children with ODD , and we per se can stimulate the brain. Other researchers show that music can have positive psychological effects in the treatment of ADHD  for use in children with comorbid ADHD and ODD, as well as preliminarily verifies that while the music intervention reinforces the effects of the yoga intervention, the latter helps the effects of the first to last longer. The results demonstrate the reasonableness of our hypotheses and the feasibility of this combined intervention in the treatment of children with comorbid ADHD and ODD. We tentatively suggest that the mechanism of action of this approach may be related to the development of the prefrontal lobe of the brain, especially because frontal lobe function impairment is a core deficit in children with ADHD . Researcin vivo . This rein vivo , thereafOur study has high practical value, and the related suggestions are as follows. Our results point out that the combined yoga and music intervention can serve as a daily adjunctive therapy for children with comorbid ADHD and ODD. Accordingly, the government could develop a yoga and music curriculum suitable for children with comorbid ADHD and ODD, organize trainings for teachers and parents on yoga and music-assisted therapy, and conduct related lectures to promote the widespread use of yoga and music therapy. Schools can arrange yoga and music programs according to the characteristics of their children, and use classroom or extracurricular activities to provide these complementary forms of therapy for children with comorbid ADHD and ODD. Parents can arrange parent–child yoga activities and music activities, which may not only ensure the continuity of intervention therapies for ADHD and ODD in the family setting but also enhance the parent–child relationship.4.1.Despite its numerous strengths and novel data on combined yoga and music interventions, this study also has some limitations. First, only one tool, the MTA SNAP-IV ADHD Rating Scale, was used to evaluate the effect of the combined intervention on children with comorbid ADHD and ODD. Future studies could use various scale types, including the Conners Child Behavior Questionnaire and the Piers-Harris Child Self-Awareness Inventory, to evaluate the effects of the joint intervention on the inattention, hyperactivity/impulsivity, and ODD of children with comorbid ADHD and ODD. This will surely provide more confirmatory evidence as to the extent and how the intervention can assist in the treatment of children with comorbid ADHD and ODD.H2 and proposed the potential action mechanism behind the combined yoga and music intervention on children with comorbid ADHD and ODD, researchers could advance this research topic  and check whether their future findings corroborate these preliminary results. We believe that there is still much room for related research, and we expect that academicians can continue to refine our understanding of the mechanisms underlying the influence of combined yoga and music interventions on ADHD and ODD symptoms in children.Second, while we did preliminarily verify Third, upon analyzing our own results, we noticed that a question was begged: what is the effect and mechanism of action of the combined yoga and music intervention on children with different subtypes of ADHD ? Our current results do not approach this topic, very few scholars have delved into it, and this is a potential focus of our own future research.Fourth, our study was a preliminary pilot study with a relatively small sample size per group. Researchers could endeavor to expand the sample size of their future studies in order to provide more evidence on the effects of a combined yoga and music intervention on children with comorbid ADHD and ODD. In addition, there is the possibility of reporting bias in the study, that future researchers could make use of more objective measures of inattention in a fully powered trial in order to deliver complementary evidence for this variable.5.This study evaluated the effects of a combined yoga and music intervention on attention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD using both the parent- and teacher-rated MTA SNAP-IV ADHD Rating Scales. The evidence suggests that the combined yoga and music intervention is a safe and effective complementary treatment for children with comorbid ADHD and ODD and can be used on a daily basis. Particularly, the combined yoga and music intervention was the most effective and had a more comprehensive effect than the yoga- and the music-only interventions. Future research on combined yoga and music interventions for children with comorbid ADHD and ODD could focus on the effects of such interventions for children with different types of ADHD and their mechanisms of action. A greater focus on combined yoga and music interventions as being safe, simple, and effective can provide us with a more comprehensive understanding of the treatment of children with comorbid ADHD and ODD.The significance and value of this study lies in its examination of the positive effects of a combined yoga and music intervention on inattention, hyperactivity/impulsivity, and ODD in children with comorbid ADHD and ODD, enriching the evidence on complementary treatments for this population. Compared with pharmacological therapy, the combined yoga and music intervention has the advantages of being side-effect free, lower cost, healthier, and more accessible, meaning that our study provides a safe and overall better option for a complementary treatment of children with comorbid ADHD and ODD.Future research could further explore the mechanisms of action of combined yoga and music in interventions for children with comorbid ADHD and ODD, compare different combined yoga and music intervention protocols, and determine which protocols are most effective in treating children with comorbid ADHD and ODD. An appropriate research question for a future study could be as follows: what are the effects and mechanisms of action of combined yoga and music interventions on different subtypes of children with ADHD ? Future studies could also collect data from larger samples and use longitudinal designs to track the long-term benefits of yoga and music for children with comorbid ADHD and ODD across the lifespan, which may allow for a broader generalization of the findings.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.All participants and their legal guardians provided written informed consent. All participants and their legal guardians were informed in detail about the design, purpose, and intent of this study. The study was approved by the Ethics Committee of the College of Physical Education, Chengdu Normal University.XL designed the study and wrote the manuscript. XH revised the manuscript. SL revised the manuscript. All authors contributed to the article and approved the submitted version.


A recent comprehensive systematic review and meta‐analysis explored the extent to which early neurocognitive and behavioural precursors are associated with the development of attention‐deficit/hyperactivity disorder (ADHD) and whether these are currently targeted in early interventions  have been instrumental for identifying common genetic variants across the genome associated with mental conditions and traits. Statistically significant findings have now emerged from GWAS for multiple traits, like educational attainment, and neurodevelopment disorders, like ADHD. For example, the latest GWAS findings from the ADHD Working Groups of the Psychiatric Genomics Consortium, identified 12 loci significantly associated with ADHD .First, the publication by Tobarra‐Sanchez et al.  in the SSecond, the study by Riglin et al.  tested tTobarra‐Sanchez et al.  and RiglFirst, PRS for ADHD and other neurodevelopmental conditions at present explain only a small fraction of variance in ADHD and early manifestations of ADHD, and is therefore a weak instrument. While larger GWAS samples will increase the predictive ability, ADHD PRS will likely never, on their own, be able to predict future diagnoses in a meaningful way. Nevertheless, it is possible that PRS in the future, through larger sample sizes and phenotypic refinement, could be used to improve outcome prediction and aid clinical decision‐making, together with other clinical variables. As suggested by Tobarra‐Sanchez et al. , future Second, because of a lack of ancestral diversity in currently available GWAS, individuals were excluded from the analyses on the basis of non‐European ancestry, which limits generalisability. Investments are needed to perform large‐scale GWASs in globally diverse populations. With more diverse samples, the genetic architecture of ADHD and other traits and conditions will be understood better globally and clinical applications of PRS will be more equally applicable across population groups.Third, while PRSs are useful for studying correlations, they cannot be taken as evidence of causality. Riglin et al.  correctlThe two publications by Tobarra‐Sanchez et al.  and RiglHenrik Larsson: Conceptualization; Writing – original draft; Writing – review & editing. Guilherme V. Polanczyk: Writing – original draft; Writing – review & editing.Henrik Larsson reports receiving grants from Shire Pharmaceuticals; personal fees from and serving as a speaker for Medice, Shire/Takeda Pharmaceuticals and Evolan Pharma AB; and sponsorship for a conference on attention‐deficit/hyperactivity disorder from Shire/Takeda Pharmaceuticals and Evolan Pharma AB, all outside the submitted work. Henrik Larsson is Editor‐in‐Chief of JCPP Advances. Guilherme, Guilherme V. Polanczyk reports receiving personal fees from and serving as a speaker and/or consultant for Abbott, Ache, Medice, Novo Nordisk, Takeda and royalties from Editora Manole. Guilherme V. Polanczyk is a Joint Editor for JCPP Advances.


Antibiotics are widely used in pregnant women and their newborns. The objective of this study was to examine the potential association between prenatal exposure to antibiotics and the risk of ADHD and ASD in childhood from a nationwide perspective. (2) Methods: The Taiwan National Health Insurance Research Database (NHIRD) was used as the primary data source. This nationwide cohort study included only first-time pregnancies. A total of 906,942 infants were enrolled. All infants were followed up for at least 6 years. The Cox regression model was applied for covariate control. (3) Results: Prenatal exposure to antibiotics was found to significantly increase the cumulative incidence of ADHD while having only a borderline effect on the cumulative incidence of ASD. Exposure to antibiotics during any of the three different gestational age ranges significantly increased the cumulative risk. However, only exposure after 34 weeks of gestation had a significant impact on the occurrence of ASD. The study also revealed a dose-dependent effect on the occurrence of ADHD but no effect on the occurrence of ASD. (4) Conclusions: This study suggests that prenatal exposure to antibiotics may increase the risk of developing ADHD and ASD later in life. Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are common cognitive and behavioral disorders that exhibit an increasing prevalence in childhood worldwide. Epidemiological and twin studies suggest that ADHD frequently co-occurs with and shares genetic susceptibility with autism spectrum disorder (ASD) and ASD-related traits ,2. BesidAntibiotics are widely used medications for infection control that have been shown to alter the gut microbiomes of pregnant women and their newborns. This exposure can lead to changes in the composition and diversity of the intestinal flora, which may impact brain–gut-microbiome interactions ,12. The Despite these findings, the relationship between prenatal or intrapartum antibiotic use, the local or systemic microbiome, and the development of ADHD and ASD are still not well understood. Specifically, the association between maternal use of antibiotics during pregnancy and the later development of ADHD and ASD in offspring has not been extensively studied. The objective of this study was to examine the potential association between prenatal exposure to antibiotics and the risk of ADHD and ASD in childhood from a nationwide perspective.This was a nationwide, population-based cohort study that used the Taiwan National Health Insurance Research Database (NHIRD) as the primary data source. Taiwan’s National Health Insurance (NHI) system was established in 1995 as a single-payer program with mandatory enrollment, covering nearly the entire population of Taiwan, estimated to be around 23.5 million individuals, with a current coverage rate of 99.99%. In 2002, the NHIRD was created specifically for research purposes and included all claims data from the NHI ,16,17. IThis nationwide cohort study included only first-time pregnancies from 2004 to 2010. Infants of multiple deliveries, preterm delivery, and those who died before reaching 5 years old were excluded.In this study, antenatal antibiotic exposure was defined as a mother who received medication with an Anatomical Therapeutic Chemical (ATC) code of J01A, J01B, J01C, J01D, J01E, J01F, J01G, J01M, J01R, or J01X during pregnancy. We also recorded the timing of the prescription, including the first, second, and third trimesters, as well as the cumulative number of prescriptions received.ADHD was diagnosed based on ICD-9 codes 314.0 (attention deficit disorder of childhood), 314.00 (attention deficit disorder without mention of hyperactivity), and 314.01 (attention deficit disorder with hyperactivity). ASD was diagnosed based on ICD-9 codes 299.0 (autistic disorder), 299.00 , and 299.01 , as well as 315.9 . Children who visited the outpatient department three times or more, or who were admitted at least once with a diagnosis, were considered to have ASD or ADHD.We also gathered data on potential confounding variables, including maternal age, mode of delivery, maternal comorbidities, maternal allergic diseases, pregnancy-related complications, and the gender of the infant.t-test, while categorical data were compared using the Pearson’s chi-square test. The cumulative incidences of ADHD and ASD were compared between groups using the Kaplan–Meier method. Hazard ratios for antibiotic prescription timing and frequency were calculated using the Cox regression model, with adjustment for potential confounders. Statistical significance was set at a p-value of less than 0.05.The data were analyzed using SAS statistical software version 9.4 . Descriptive statistics were reported as means with standard deviations, or as frequencies and percentages. Continuous variables were compared using an independent A total of 906,942 infants were enrolled in the study cohort after applying the exclusion criteria . This naAmong the 906,942 infants enrolled, 53.4% had a history of antenatal antibiotic exposure. The demographic data are presented in The Cox regression models were utilized to adjust covariates, and the results are presented in In order to investigate the impact of prenatal antibiotic exposure, we conducted further analysis of the timing and dosage of exposure. Additionally, we explored the dose effects of prenatal antibiotic exposure and summarized our results in This nationwide population-based study established a positive association between exposure to prenatal antibiotics and the subsequent development of ADHD and ASD during childhood. The study also examined the relevance of the timing of exposure and the dose effect on the development of these conditions. While a dose-dependent effect was observed in ADHD, no such effect was found in ASD. Late preterm and term infants showed the most significant impact of antibiotic exposure on the development of ASD. The study has several strengths, including a large sample size of 906,942 participants and a longitudinal follow-up of at least 6 years. The information collected from our database was comprehensive, and the database enrolled practically all newborns during this period, without discrimination on the basis of social/economic status or region of residence. Moreover, access to health care and medical resources is universal in Taiwan. Thus, diagnoses of ADHD or ASD would not have been overlooked for children in lower social/economic classes. Furthermore, the study employed multiple regression models to control for confounding factors, such as maternal age, type of delivery, maternal comorbidities, and common pregnancy-related complications, and assessed their roles in the development of ADHD/ASD.Numerous studies have investigated the relationship between early-life exposure and the development of ADHD or ASD. A Canadian population-based study utilized multiple databases to examine the association between antibiotic exposure in the first year of life and the risk of ADHD later on. The study found that exposure to antibiotics during the first year of life was linked to an increased risk of ADHD . HoweverAnother twin study was conducted to investigate the association between early-life antibiotic exposure and the risk of developing ADHD and ASD. The findings revealed a positive association between antibiotic use and increased risk of these disorders which was attenuated in monozygotic twins. These results suggest the potential for confounding by a shared familial environment and genetic factors during early development . A systen = 187,605) compared to our study. Furthermore, this study did not exclude preterm neonates, whose immature brain and nervous systems and prematurity-associated conditions, such as asphyxia and congenital infections, may have influenced the results [A cohort study was conducted to investigate the relationship between prenatal antibiotic exposure and the risk of ADHD using a sibling analysis approach. The results showed an increased risk of ADHD in the overall and matched cohorts, but not in the sibling cohort. The authors concluded that there was no significant association between prenatal antibiotic exposure and the risk of ADHD later in childhood. However, the study had a relatively small sample size  which examined the association between prenatal antibiotic exposure and the risk of ASD, a small increase in ASD risk was observed following prenatal exposure. The authors also noted the limited clinical significance of this finding, although it was statistically significant and consistent with the results of our study. This study also found higher risk in those exposed to prenatal antibiotics for longer durations, which was compatible with the findings of our study [In another population-based survey with a relatively smaller number of cases (ur study .ASD is a neurodevelopmental condition characterized by impairments in social interaction, social communication, and the presence of restricted and repetitive patterns of behavior, interests, and activities . The preOver the past few decades, researchers have dedicated their efforts to understanding the pathomechanism of ASD to identify novel and more effective therapeutic targets ,32. WhilOur study had several limitations that require consideration. First, the NHIRD of Taiwan was our data source, resulting in a predominantly Asian cohort. The restricted diversity in ethnicity may have contributed to disparities in our findings compared to analogous studies conducted in Western countries ,23. SecoOur findings suggest that prenatal exposure to antibiotics may increase the risk of developing ADHD and ASD later in life. This discovery constitutes an advancement in unraveling the intricate pathogenesis of ADHD and ASD. Furthermore, it shows the potential for identifying preventive strategies specifically designed to mitigate the incidence of ADHD and ASD. However, further prospective follow-up studies are necessary to fully elucidate the association between prenatal antibiotic exposure and the development of these disorders.


Attention-Deficit/Hyperactivity Disorder (ADHD) is young children’s most common mental health disorder. We aim to provide causal estimates of the differential costs for the non-adult population with ADHD. We used longitudinal administrative data covering the non-adult population over five years and different healthcare providers  of 1,101,215 individuals in Catalonia (Spain). We also include the consumption of pharmaceuticals and cognitive therapies. We instrumented ADHD diagnosis by the probability of being diagnosed by the most visited healthcare provider based on individual monthly visits to the provider in which this visit was related to ADHD and the density of professionals in the different mental health providers. After using matching procedures to include a proper control group, we estimated two-part and finite mixture models. Our results indicate that ADHD children and adolescents displayed 610€ higher annual health direct costs compared to not diagnosed counterparts. We provide average costs disentangling the sample by age boundaries, gender, and comorbidities to offer values for cost-effective analyses and incremental costs after diagnosis, which is around 400€. A significant differential annual direct health cost for the non-adult population with ADHD is determined, which will be helpful for cost-effectiveness analysis and complete cost-of-illness studies. Attention-Deficit/Hyperactivity Disorder (ADHD) is young children's most common mental health disorder. Prevalence rates worldwide range from 3 to 7% (average of around 5%) of school-aged children , and theThis disorder has a functional impact on children and adults . The natFor these reasons, studies of healthcare costs are essential to help policymakers to plan mental health services and treatment. It is well known that the non-adult population with ADHD consumes more healthcare resources than those without ADHD . HoweverThe main contribution of this paper is to provide causal estimates of the differential costs for the non-adult population with ADHD instrumenting ADHD diagnosis by the probability of being diagnosed by the most visited healthcare provider based on individual monthly visits to the provider in which this visit was related to ADHD and the density of professionals in the different mental health providers. That is, we use supply information to identify these differential healthcare costs correcting our estimates by instrumental variables (IV) to avoid the influence of confounders. For this purpose, we use the whole population in Catalonia, both those diagnosed with ADHD and those without, and provide average annual costs for different subgroups of the population disaggregated by age and gender, in addition to considering some comorbidities that might induce higher costs. Further, we provide information about incremental costs after being diagnosed and heterogeneity according to adherence to medication.Cost analyses differ greatly depending on the country analysed, treatment choice, and the database used –9, althoThe limited number of European studies examining the economic burden of ADHD highlights the need for research in this area. A systematic review  pointed A recent systematic review of the global economic burden of ADHD  estimateInterestingly, costs increase with age. Subgroup analyses were performed by age  and sex. This project  concludeOther studies compare costs before/after diagnosis. In this regard, patients with ADHD show higher resource utilisation compared to a control group , even beHowever, there are many limitations concerning the abovementioned studies for different reasons: calculations from averages, preliminary designs, biased samples, use of cohorts with ADHD, databases related to US insurers, lack of detailed clinical information, the inclusion of comorbidities or even more critical, non-consideration of appropriate controls. These limitations, such as not including other comorbidities, will bias the differential effect of ADHD on individual costs. We seek to address some of these limitations in this study.The Ethical Review Board approved the study in Hospital Trueta & IAS, Girona (Spain). We use a large administrative dataset from The Agency for Health Quality and Assessment of Catalonia that includes information from several providers, although considering different periods for the whole population of Catalan children born between 1998 and 2012, including those diagnosed with ADHD and those not being diagnosed. We focussed on cohorts of children over six years old because they are unlikely to get an ADHD diagnosis before this age .This database contains information on primary care, hospitalisations, emergency care, mental health hospitalisation, and community mental health care from 2013–2017. It also encompasses an individual identifier, the visit date , and all diagnoses and procedures registered in these visits. The International Classification of Diseases, ninth revision, Clinical Modification (ICD-9-CM) diagnostic manual is used for diagnostic purposes in Catalonia. Diagnoses are shown in an ordinal sense, indicating the primary diagnosis for that visit and a list of secondary diagnoses. Via unique personal identifiers, the information is linked between all provider's datasets but also to some demographic information: gender, age, drug co-payment level, which is related to the socioeconomic status of their parents, individual nationality, date of death and the sanitary health region they belong to.There are 2,800 healthcare procedures (HCP) in the dataset defined and classified according to the ICD-9-CM. The unit cost of each HCP has been imputed using a complete list of public prices approved in 2013 by the Department of Health in Catalonia (Spain) for the Catalan Healthcare Service for primary care services, hospital and specialised services, and psychiatric and mental health.No list of public prices has been officially published for Catalonia since 2013. However, after 2013, unit prices for hospital and specialised visits were actualised or incorporated until 2020. However, very few prices for healthcare procedures have been included. Therefore, the approved public prices for 2013 are used as the leading resource to impute the cost of the HCP. Public prices approved in 2013 for primary care include unit prices or tariffs for standard primary care services such as GP visits, ambulatory care, or domiciliary care. In the case of hospitals and specialised services, most of the prices for HCP are set according to the Diagnostic Related Group (DRG), including prices for a wide range of surgical procedures, implants, infections, plastic surgery, etc. Other hospital and specialised services tariffs include rehabilitation and physiotherapy prices, laboratory test prices, and further tests, procedures, and therapies undertaken as part of the primary diagnosis.Prices set by the Department of Health in Catalonia for each DRG vary according to the group of hospitals they are paid to. The Department distinguishes four groups of hospitals and classifies each Catalan hospital into one of these four groups according to the resource used and their structural capacity . Therefore, for the same DRG, the Department of Health sets a different price according to each group of hospitals: a unified price for group 1  and 2 , a price for those hospitals belonging to group 3 , and a price for those classified in group 4 . There are 72 hospitals in the dataset, and information is provided on which hospital individuals have been treated every time they require hospital care. Therefore, it is possible to identify which group each belongs to and impute the corresponding DRG costs established for that group of hospitals. We show in Table A1 in the https://www.ine.es/prensa/ipc_tabla.htm. Instituto Nacional de Estadística).However, the Catalan Health Service does not provide a tariff for all healthcare procedures. In such a case, the DRG price is assigned where a healthcare procedure may belong and be performed. As explained above, since groups of hospitals set prices for DRGs, HCPs will have different prices according to the hospital where they performed. Moreover, if an HCP occurs in more than one DRG, the average across all the possible DRGs by groups of hospitals is taken. Therefore, the price inputted for a particular HCP can be either a special tariff, differing by a group of hospitals, or an average of different DRG prices. If neither the unit price nor the DRG price by the hospital’s group is found for Catalonia, the price of healthcare procedures is approximated by searching for prices of other regions in Spain in any available year. The platform Esalud has used an online and up-to-date database of reported Spanish healthcare costs. Prices in Esalud are deflated using either the 2018 or the 2019 consumer price index. Therefore, to homogenise prices, we input the actualised price. If the price for an HCP is found for more than one Spanish region, we average their corresponding actualised prices. Overall, 93% of the prices have been found, where 66% come from a single tariff  and 27% come from DRGs and groups of hospitals. Since the dataset encompasses HCPs from 2013 to 2017, imputed prices are deflated to 2017, using the corresponding Consumer Price Index for each year . We further excluded those individuals whose total average yearly costs were above the 95Finally, we compute the differential and incremental costs. The differential cost is defined as the additional annual cost that an individual with ADHD faces in the national health service. In contrast, the incremental cost is the annual cost that an individual with ADHD experiences during the period after diagnosis compared to the period before the first diagnosis.To compute the differential cost of a non-adult individual with ADHD, we must consider two relevant factors: (i) the econometric technique to be used and the control units, and; (ii) the factors associated with higher costs to be included in the regressions and the inclusion of specific comorbidities. The first factor is the asymmetric distribution of the variable costs and the methodology's specific bimodality condition. We have estimated it using different alternatives: neighbourhood matching , propensity score matching, and two-part models. All of these models provide complementary approaches. The first two methods focus on finding units that act as a control . At the same time, the third one considers the estimation of the incremental cost in the two parts of the distribution (low costs as opposed to high costs). The latest model has also been estimated considering the individuals identified as controls using the neighbourhood matching technique for robustness. This technique allows us to run through the list of treated units and select the closest eligible control unit to be paired with each treated unit for a homogeneous comparison. Finally, we also considered estimating using finite mixture models (FMM), which usually accommodate the case of costs better given their high asymmetry and the presence of individuals with high costs that complicate average comparisons. FMM is useful to model the probability of belonging to each unobserved group  to estimate distinct regression model parameters in each group. We considered two latent classes to explain the different distribution parts in this case. For our approach, the first part of the positive costs is relevant, that is, the one not affected by heavy users or healthcare resources.propi) [profi). Once the most frequented supply unit has been identified, this ratio of professionals is assigned. The rationale behind this second instrument is that a higher supply of professionals could reduce those not correctly diagnosed and thus reduce the percentage of those diagnosed [Yi indicates the average of the total direct health costs of the individual pooled during the period 2013–2017 and, while Xi is a set of observable characteristics  and some comorbidities at the level of the year such as the existence of visits due to: overweight, asthma, learning disability, depression, anxiety, conduct disorder and challenging disorder. Age was non-linearly introduced. Copayment levels constitute an appropriate proxy for income levels, given that copayment percentages are defined based on parental income levels [diagi is a dummy variable taking the value one if an individual is diagnosed. We include fixed effects that may affect prescribing, i.e., basic health areas for the health sectors (absi). Models also considered error terms . Both equations were estimated simultaneously, and standard errors were clustered at the abs level because most healthcare decisions and experiences are shared at this aggregate level. Akaike information criteria showed that the two-part model was the best estimation procedure.Our identification strategy is based on instrumenting ADHD diagnosis by the probability of being diagnosed by the most visited healthcare provider based on individual monthly visits to the provider in which this visit was related to ADHD (propi) . The inspropi) . This toiagnosed . This ste levels . diagi iTable We observe that the cost distribution is not strictly comparable across all individuals. Figure Table Measuring potential over-diagnosis or improper classification of ADHD is essential. We established several definitions of children diagnosed with ADHD: (i) those diagnosed with the ICD-9 codes related to ADHD in any healthcare provider, (ii) children consuming drugs related to the disease, and (iii) a classification informed by clinical expertise. Specifically, concerning the latter, we categorised children into three groups: (i) highly likely ADHD diagnosis, (ii) potentially likely ADHD diagnosis, and (iii) not very likely ADHD diagnosis. A highly likely ADHD diagnosis occurs when we find a principal ADHD diagnosis in mental health centres or inpatient mental health units from the comprehensive Catalan public health system (SISCAT). Potentially likely ADHD diagnosis is when we identify an ADHD pharmacological treatment and secondary ADHD diagnosis in mental health centres or inpatient mental health units or ADHD diagnosis in primary care . Then, we restricted our analysis to those individuals classified as highly likely ADHD diagnosis or potentially likely ADHD diagnosis to avoid overdiagnosis-related concerns. In this case, our results revealed a higher difference of 779.6€ (749.9€-809.2€) than the one obtained using the non-specific definition (609.8€). For those potentially likely, the results evidenced a higher difference of 585.3€ (564.4€-606.3€).Next, we evaluated the cost differences between the year before and the years following the first diagnosis. We report these figures through this section. In this case, we compare the total expenditure, again eliminating extreme cases, in the annual period before the first diagnosis and comparatively annually up to five years after that diagnosis only for children and adolescents with ADHD. Descriptively, the increase is evident in the first year after diagnosis (392.2€), and from that year, some downward stabilisation to 380.1–385.3€ seems evident. However, we adjust these differentials compared to the annual cost before diagnosis, conditional on the characteristics of the individuals. Again, the distributions are asymmetric, so estimates using ordinary least squares and GLM  have been compared. Comparing the fitting performance of these models using the Akaike criterion, the GLM estimate shows the best results. Thus, compared to the initial period and adjusting for the characteristics of the individuals, the costs are higher: 413.3€ (385.5€-441.1€) after one year, 401.1€ (376.2€-426.0€) after two years, 371.6€ (347.8€-395.3€) after three years, 365.4€ (341.9€-388.9€) after four years and 366.9€ (342.0€-391.8€) after five years.We also account for the impact of adherence on the differential cost but exclude ADHD drug costs from overall costs . We measured adherence to ADHD drugs by computing either the medication possession ratio (MPR) or the proportion of days covered (PDC). These are standard measures to compute adherence. Additionally, we used a dummy representing those with complete adherence to ADHD medication. Results are shown in Table This study estimated the differential direct healthcare costs of the non-adult population with ADHD in Catalonia (Spain). Indeed, we contribute to the scarce literature within the European context on ADHD costs. More importantly, no previous studies have assessed the economic impact of ADHD on the utilisation of healthcare resources accounting for longitudinal administrative data and using a method that allows us to identify causal differential healthcare direct costs conditional on an extensive list of comorbidities that might alter this differential effect . Likewise, to measure resource consumption, we considered a complete list of public healthcare providers , including all relevant resources .According to previous research , our datOne of our study's limitations is that we could not consider private use healthcare costs and costs related to diagnostic tests and referrals. This information is still not available. Additionally, we can’t disentangle which type of ADHD was predominant within ADHD-diagnosed individuals because the diagnosis codes at that date did not differentiate this issue. Next, ICD-9 was the criteria implemented in Catalonia during the analysed period. We acknowledge that a different standard, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), could have resulted in a different prevalence of the non-adult ADHD population and affected our differential cost findings. For this reason, we have included a correction that might avoid potential overdiagnosis impacting direct health costs related to the non-adult ADHD population. Finally, the consumer price index was a limitation, given that an appropriate public health services price index is not allowable in Spain.The data on the use of services in treating ADHD showed heterogeneity in services and treatment pathways. If improvements are introduced to reduce the variability of clinical practice in treating ADHD, having this cost reference value will allow us to monitor costs and compare them with a previous reference value. This can contribute to the continuous improvement of ADHD treatment care planning. Finally, incremental costs after diagnosis are estimated at around 400€. This cost estimate highlights the importance of early detection of ADHD to reduce future healthcare costs.