Objective: Autism spectrum (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders with a high rate of comorbidity. To date, diagnosis is based on clinical presentation and distinct reliable biomarkers have been identified neither for ASD nor ADHD. Most previous neuroimaging studies investigated ASD and ADHD separately.Method: To address the question of structural brain differences between ASD and ADHD, we performed FreeSurfer analysis in a sample of children with ADHD (n = 30), with high-functioning ASD (n = 14), with comorbid high-functioning ASD and ADHD (n = 15), and of typically developed controls . With FreeSurfer, an automated brain imaging processing and analyzing suite, we reconstructed the cerebral cortex and calculated gray matter volumes as well as cortical surface parameters in terms of cortical thickness and mean curvature.Results: A significant main effect of the factor ADHD was detected for the left inferior frontal gyrus  volume, with the ADHD group exhibiting smaller Pars orbitalis volumes. Dimensional measures of autism  and ADHD (DISYPS-II FBB-ADHD score) had no significant influence on the left Pars orbitalis volume. Both, ASD and ADHD tended to have an effect on cortical thickness or mean curvature, which did not survive correction for multiple comparisons.Conclusion: Our results underline that ADHD rather than ASD is associated with volume loss in the left inferior frontal gyrus . This area might play a relevant role in modulating symptoms of inattention and/or impulsivity in ADHD. The effect of comorbid ADHD in ASD samples and vice versa, on cortical thickness and mean curvature, requires further investigation in larger samples. Previous studies mainly investigated autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in isolation. The question to what extent there is a clinical overlap between ADHD and ASD, whether they represent distinct diagnostic categories or form a continuous disorder with a continuum of brain dysfunction remains open to discussion , 2. To iASD is characterized by impairment in social communication and interaction as well as by a range of stereotypic behaviors whereas deficits in attention, hyperactivity, impulsiveness, disorganization and affective instability represent the core symptoms of ADHD . As ASD Studies report frequent co-occurrence with 30-50% of individuals with ASD manifesting ADHD symptoms (particularly at pre-school age) and two-thirds of individuals with ADHD showing features of ASD . Social Previous structural neuroimaging studies encompassing children and adolescents with both, ADHD and ASD, are scarce and show heterogeneous results Table . There iVoxel based morphometry (VBM) studies display a heterogeneous picture. The most recent VBM study suggested GM reduction in the right posterior cerebellum to be disorder-specific for ADHD relative to ASD. GM enlargement in the middle/superior temporal gyrus, on the other hand, was reported to be disorder-specific for ASD relative to ADHD . An earlIn ASD it has frequently been reported that after having a normal  or smallDespite similarities in clinical presentation as well as mutual comorbidity rates in ASD and ADHD, these disorders present a rather different neuroanatomical profile. Most studies report subcortical temporal structures such as the amygdala to be enlarged in young children (at ages 2–4) with ASD  with a nStudies focusing on cortical surface parameters in ASD also reported mixed results. When investigating cortical surface parameters, it has to be reflected that cortical development in ASD varies across developmental stages or brain regions. Three different phases have been proposed: accelerated expansion in early childhood, accelerated thinning in later childhood and adolescence, and decelerated thinning in early adulthood . HazlettIn children and adults with ADHD, cortical thinning has been described in parietal and frontal regions responsible for executive function and attention –42. AnotThe unclear and puzzling current state requires further studies directly comparing volumetric and cortical thickness parameters between ASD and ADHD. Individuals with isolated autism and individuals, who present comorbid conditions in terms of ADHD, can be distinguished behaviorally as already documented by our research group .Based on the available evidence, we aimed to study the brain structure in children with ASD with and without comorbid ADHD as well as TD. To address the question of a potential neurobiological overlap between ADHD and ASD, we analyzed the brain scans for shared and disorder-specific abnormalities. We investigated differences in terms of GM as well as cortical thickness and mean curvature.In doing so, this study represents the first FreeSurfer study comprising ASD and ADHD groups, as well as subjects with co-occurrence of both conditions. Because previous studies showed inconsistent and widely distributed changes, we did not limit the analysis to individual a priori regions of interest (ROIs).The ethics committee of the University Medical Center Freiburg approved the study . Magnetic resonance imaging (MRI) scans were acquired following written informed consent of the children's parents. Male children with ASD and ADHD were recruited from the Department of Child and Adolescent Psychiatry, Psychotherapy, and Psychosomatics of the University Medical Center Freiburg.We obtained scans of high quality in 40 male children with a diagnosis of ASD according to ICD-10 and DSM-5 criteria. Twenty-nine ASD patients were included in the final analysis after 7 patients were excluded due to image artifacts, 3 due to IQ < 70 and one patient due to comorbid seizures. All ASD children were high-functioning and with no language delay. Intellectual disability , comorbid Tourette syndrome or severe neurological diseases were defined as exclusion criteria. With the exception of 2 patients, no ASD participant had comorbid depressive or anxiety symptoms. The diagnostic process followed international guidelines, including the Autism Diagnostic Observation Schedule  and theAdditionally, MRI-scans of 50 male patients with ICD-10 and DSM-5 ADHD diagnosis without a comorbid ASD were acquired. Twelve scans were excluded due to poor image quality, 7 due to the low IQ of the subjects (< 70) and one due to an arachnoid cyst, so that finally 30 ADHD patients were analyzed. ADHD diagnosis was clinically based on ICD-10 and DSM-5 criteria and additionally confirmed with the DISYPS-II FBB-ADHD , also thFourty-eight typically developed male children (TD) were recruited from local schools and sport groups. Control subjects were included after a phone interview with the parents who additionally completed a sociodemographic questionnaire, the CBCL  and the Subjects were matched according to IQ assessed with Raven's Standard Progressive Matrices , age andAll subjects included in the study accomplished behavioral tasks of executive functions and planning as well. The results are published elsewhere .3.A standard magnetization-prepared rapid gradient echo (MPRAGE) T1-weighted anatomical scan was conducted  on a 3T Siemens TIM Trio Magnetom scanner . Slice thickness was 1 mm and voxel size 1 × 1 × 1 mmhttp://surfer.nmr.mgh.harvard.edu/). FreeSurfer is a fully automated suite of tools that enables analysis of key features in the human brain such as segmentation of most macroscopically visible brain structures .Cortical reconstruction and segmentation was performed using FreeSurfer version 5.3  or for which the segmentation procedure failed, were excluded.Individual brains were registered on a spherical atlas for parcellation, taking into account individual cortical folding patterns to match brain geometry between the subjects. FreeSurfer parcellated each brain into 148 GM and 32 subcortical ROIs using the Desikan-Killiany-Atlas . AfterwaCortical surface area was calculated with FreeSurfer based on a 2D representation of cortical surface after estimation of GM/WM boundary and pial surface . CorticaGroup comparisons of demographic and psychometric data  were carried out using SPSS software, version 22 . We used analysis of variance (ANOVAs) for the assessment of significance of putative differences.Further analysis of imaging data was carried out using R statistical computing software . We testWe adjusted volume, mean curvature and thickness data for differences in age and IQ using a linear model applying the groups mean age and IQ.p < 0.05 were considered significant, and uncorrected p < 0.05 were regarded as trends. We didn't restrict our analysis to a priori regions of interest.Type III two-way 2 × 2 ANOVAs on the adjusted volume, thickness and curvature data were calculated using the independent between-subject factors ASD diagnosis (yes vs. no) and ADHD diagnosis  = 12.63; pFDR = 0.039, puncorr < 0.001]. Children with an ADHD diagnosis exhibited smaller left Pars orbitalis volumes .Due to the heterogeneity of findings in earlier studies, we did not limit our analysis to a priori regions of interest.The diagnosis ADHD has a significant effect on the left Pars orbitalis volume with ADHD-diagnosed children showing smaller left Pars orbitalis volumes. These findings suggest that ADHD rather than ASD is related to left Pars orbitalis volume loss. Whether there are weaker “additive” effects on the Pars orbitalis volume of ASD and ADHD cannot be ruled out with a study of the given sample size.On trend level, we additionally found an ADHD main effect for the right Pars orbitalis and precuneus cortex and an interaction of diagnosis ADHD and ASD for the right isthmus cingulate cortex.The so-called default-mode network (DMN) has been described as comprising the precuneus/posterior cingulate cortex, the medial prefrontal cortex and the medial, lateral and inferior parietal cortex. It is a network of brain regions associated with task-irrelevant mental processes and mind wandering , 60 In lThe Pars orbitalis represents a subdivision of the inferior frontal gyrus which more or less corresponds well to the Brodman Area 47.Functionally it has been linked to the recognition of facial expressions of basic emotions  and to tChildren with ADHD and autism have a lot of similar features and there is high frequency of ADHD symptoms in autism . Also, iIn a recent publication the Pars orbitalis has been implicated as being part of a critical network for the identification of specific ADHD/ASD subtypes .The volume reduction in the Pars orbitalis is partly consistent with one previous study which reported a trend toward a lower Pars orbitalis volume in ADHD, but in this investigation, the right Pars orbitalis was affected . In the Additionally, VBM studies examined adolescent samples. Only the study by Mahajan et al.  studied Multiple regression models with the independent variable SRS total score or DISYPS-II FBB-ADHD revealed no significant effect of these scores or their interaction with the factors ADHD and/or ASD on left Pars orbitalis volume. It can therefore be assumed that the reduction of the left Pars orbitalis volume is a categorical effect due to ADHD diagnosis and is not due to the severity of different symptoms or traits represented by questionnaires.No significant interaction or main effects for the factors ASD or ADHD on cortical thickness or mean curvature could be detected after FDR correction. The fact that significant group effects only emerged on an uncorrected level might indicate that the effect sizes of possible differences are too small to be detected with the present group size. We decided to interpret the uncorrected significant differences as possible trends.In doing so, a main effect for ASD diagnosis emerged for the bilateral post-central, left pericalcarine, left cuneus as well as right superior parietal cortical thickness, and an effect for the factor ADHD resulted for the left Pars orbitalis thickness. An interaction of ASD and ADHD diagnosis was detected for left inferior parietal, parahippocampal, pericalcarine, transverse temporal, and right post-central thickness measures.Our results of cortical thinning in several areas in ASD children (aged 6–13 years) are concordant with studies reporting accelerated thinning in childhood ASD . GreaterThe detected cortical thinning in the superior parietal cortex in ASD relates well with previous investigations , 33. TheThe definite pathomechanisms resulting in cortical thinning are not yet clarified. Regressive  and progressive  events are supposed to result in the appearance of GM density reduction or cortical thinning , but furThe study was conducted with a sample of children with ASD or ADHD being prone to motion artifacts . TherefoDue to the study's focus on primary forms of ASD the results presented here can not necessarily be generalized to forms of ASD with intellectual impairment or to syndromal-secondary autism (defined as autism with known etiology) . AdditioMethylphenidate medication was discontinued at least 24 h prior to scanning procedure. Evidence suggests an impact of long-term neurotropic medication on brain structure with stimulant medication being associated with normalization of structural abnormalities in ADHD . This coIn summary, we detected that ADHD rather than ASD mediates volume loss in the inferior frontal gyrus . The volume reduction in the left Pars orbitalis seems to be primarily a categorical diagnostic effect than to reflect the severity of various traits or symptoms. ASD and ADHD diagnoses tended to have an effect on cortical thickness or mean curvature, which did not survive correction for multiple comparisons. Further studies of more power in larger samples are necessary to investigate the effect of ADHD and ASD on cortical thickness and mean curvature. Additionally, further research is needed to disentangle the precise causal pathways.MB is principal investigator of the study funded by German Federal Ministry of Education and Research . MB, JU, RR, IM, CK, and CPK designed the study and were responsible for acquisition and analysis of data. SM, KN, and JM performed FreeSurfer analysis of the data. KN, SM, JM, MB, DE, AR, and LTvE were crucially involved in the theoretical discussion and preparation of the manuscript. KN and SM wrote the manuscript. All authors read and approved the final version of the manuscript. They agreed to be accountable for all aspects of the work.LTvE advisory boards, lectures, or travel grants within the last 4 years: Eli Lilly: Janssen-Cilag, Novartis, Shire, UCB, GSK, Servier, Janssen, and Cyberonics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The aim of our study was to find distinctive characteristics of handwriting in children with ADHD by using graphology to analyze physical characteristics and patterns, and to evaluate whether graphological analysis is an effective ADHD diagnostic tool for cliniciansThe cohort included 49 children aged 13–18 years attending a tertiary neurology and epilepsy center in 2016–2017; 22 had a previous DSM-IV/V diagnosis of ADHD. The children were asked to write a 10–12-line story in Hebrew on a blank sheet of paper with a blue pen over a 20-min period. The samples were analyzed by a licensed graphologist blinded to the clinical details of the children against a predetermined handwriting profile of individuals with ADHD. Each ADHD characteristic identified in each sample was accorded 1 point, up to a total of 15 points. Patients with a graphology score of 9–15 were considered to have ADHD.+ 3.49 vs. 5.79 + 4.01, p = 0.002, respectfully). Using a score of 9 as the cutoff, in the girls, graphology had a specificity of 80% (95% CI 59.2–92.8) and a of sensitivity 71.4% for predicting ADHD. Corresponding values in the boys were 75.0 and 76.2%.There were 21 boys (43%) and 28 girls (57%) in the cohort; 15 boys (71.4%) and 7 girls (25%) had a DSM-IV/V diagnosis of ADHD. The mean graphology score was significantly higher in the children who had a DSM-IV/V diagnosis of ADHD than in the children who did not (9.61 The handwriting of children with ADHD has specific characteristics. Graphology may serve as a clinically useful tool in the diagnosis of ADHD. These findings may reflect poor motor skills and visual-motor integration, which are directly correlated with low handwriting legibility [Handwriting difficulties are common in children with attention deficient hyperactive disorder (ADHD) and have been associated with lower academic achievement and self-esteem –3. Teachgibility . Furthergibility , 4.Tucha and Lange  studied Graphology is an ancient discipline developed in China for purposes of analyzing the personality and behavior of individuals through the physical characteristics and patterns of their handwriting . FollowiSince the mid-twentieth century, graphology has been applied in many settings: to find suitable employees, establish the authenticity of a signature or text, and establish the state of the author of a signature or a text, . It has also been used in court and during criminal investigations. Handwriting examinations are recognized clinical tools in psychiatry  for the We hypothesized that children with ADHD might be distinguished by the types and number of abnormalities found in their handwriting. The aim of the present study was to use graphology to analyze the characteristics and patterns of handwriting in children diagnosed with ADHD compared to children without ADHD and to determine if graphology might serve as an objective auxiliary tool in the diagnosis of ADHD.A total of 49 children aged 13 to 18 years were included in the cohort. The study group consisted of 22 children who presented at the clinics of Meuhedet Health Services (Northern District), one of the four publicly funded health maintenance organizations in Israel, between September 2016 and September 2017. All were diagnosed with ADHD by a senior pediatric neurologist using teacher and parent questionnaires followed by interviews with the child and at least one parent. The final diagnosis was based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th (1994) or 5th (2013) edition (DSM IV/V) , 12. TheThe study was approved by Helsinki Committee of Rabin Medical Center. The parents or guardians of all children provided written informed consent prior to enrolment in the study.Handwriting samples were collected from all participants. The children were given a blank sheet of paper and a blue pen and asked to write a story in Hebrew of 10–12 lines over a 20-min period. The papers were collected and submitted for analysis to a licensed forensic graphologist (B.C.-K.) who was blinded to the background and clinical data of the subjects.The graphologist established a predefined handwriting profile of individuals with ADHD based on graphology theory that handwriting can determine the type of personality and evaluated each sample accordingly. The profile was composed of 15 characteristics: text layout (spread out); margins (none or only one); line direction (never ascending); line, word and, letter spacing ; nonconventional letters (many); handwriting slant (never to the right or ascending); deviation of handwriting (yes), letter size ; letter width (only wide or only narrow); continuity or flow connection (absent), shape of writing (never thread-shaped), writing speed (never slow), and strength of graphism (poor). In each sample, 1 point was accorded for every abnormal characteristic identified, yielding a graphology score ranging from 0 to 15. Patients with a score of 9 or more were considered to have ADHD.p < 0.05. Chi-square test was used to compare categorical variables.The graphology scores of the patients with and without ADHD were summarized as mean and standard deviation and compared between the groups, total and stratified by sex. Since scores showed a non-normal distribution, the non-parametric Mann-Whitney U test was used for data analyses. All tests were two-tailed, and the level of significance was set at The diagnostic accuracy of the graphology evaluation was examined using receiver operating characteristic (ROC) curve analysis, which depicts sensitivity by 100% specificity for every possible cutoff score, with a resulting area under the curve (AUC) ranging from 0.5 (no better than chance) to 1 (perfect diagnostic accuracy). An AUC of 0.8 or higher suggests that an instrument can be considered a useful screening tool . Sensitip = 0.204) and more likely to be male  .The total cohort included 22 boys (44.9%) and 27 girls (55.1%) of whom 14 boys (66.7%) and 7 girls (33.3%) had ADHD. Nine children (2 girls) had combined-type ADHD . The subjects with and without a DSM IV/V diagnosis of ADHD were comparable in age distribution  , motor planning, fine motor skills, and visual motor perception . Lerer ea et al.  who repoa et al. , leadinga et al. . Other sa et al. , 20, buta et al. . Among ta et al. .Thus, our study showed that handwriting evaluation by a graphologist can have incremental validity in terms of diagnostic accuracy in children with ADHD and can contribute to decision-making by the multidisciplinary team, especially in complicated cases Li-Tsang et al.  suggeste.The main limitations of the present study are handwriting analysis by only a single graphologist, small sample size, and lack of data on other background factors such as socioeconomic class and parental education. Larger scale studies with multiple blinded graphologists are needed to corroborate our findingsThe handwriting of children with ADHD has specific characteristics. The use of graphology for the diagnosis of medical disorders is a highly disputed branch of handwriting analysis. It is likely that handwriting problems in ADHD have less to do with the writing itself and more to do with factors related to motor control. Our study suggests that graphology is a promising potential auxiliary tool for use by clinicians in the diagnosis of ADHD.Additional file 1: Files are related to Availability of data and material.


Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 8%–12% of children worldwide. Throughout an individual’s lifetime, ADHD can significantly increase risk for other psychiatric disorders, educational and occupational failure, accidents, criminality, social disability and addictions. No single risk factor is necessary or sufficient to cause ADHD. The multifactorial causation of ADHD is reflected in the heterogeneity of this disorder, as indicated by its diversity of psychiatric comorbidities, varied clinical profiles, patterns of neurocognitive impairment and developmental trajectories, and the wide range of structural and functional brain anomalies. Although evidence-based treatments can reduce ADHD symptoms in a substantial portion of affected individuals, there is yet no curative treatment for ADHD. A number of theoretical models of the emergence and developmental trajectories of ADHD have been proposed, aimed at providing systematic guides for clinical research and practice. We conducted a comprehensive review of the current status of research in understanding the heterogeneity of ADHD in terms of etiology, clinical profiles and trajectories, and neurobiological mechanisms. We suggest that further research focus on investigating the impact of the etiological risk factors and their interactions with developmental neural mechanisms and clinical profiles in ADHD. Such research would have heuristic value for identifying biologically homogeneous subgroups and could facilitate the development of novel and more tailored interventions that target underlying neural anomalies characteristic of more homogeneous subgroups. Attention-deficit/hyperactivity disorder (ADHD) is among the most commonly diagnosed neurodevelopmental disorders, affecting approximately 8%–12% of children worldwide, with up to 65% continuing to have ADHD symptoms and neuropsychological impairments in adulthood , emotion lability-based sub-types were also suggested. Deficient emotional self-regulation has been suggested to be a core component of ADHD Barkley, . A recenNevertheless, categorical classification system has its shortcomings regarding to what is the best cut-off thresholds of the symptoms characterized as a dimension, the large number of intermediate cases, comorbidities, etc.  cognitive processes, including encoding, central processing and response organization; (2) energetic pools, including arousal, activation and effort; and (3) management/EF, which encompasses both top-down and bottom-up processes and draws attention to the fact that ADHD is linked to defects at these three levels (Sergeant, Thus, the CEM describes suboptimal performance in ADHD as a response modulation deficit that is triggered by contextual factors such as event rate. It explained a phenomenon that performance can be improved to normal levels under conditions that are thought to increase arousal and/or motivation, such as the provision of external rewards (Konrad et al., In the past decade, the neural mechanisms that determine the diverse developmental trajectories of ADHD have been increasingly investigated. Halperin and Schulz  proposedIn summary, this section reviewed three theoretical models of ADHD; cognitive and motivational impairment models, the CEM, and the neurodevelopmental model. Each of these models partially explains the symptomology of ADHD. In particular, the working memory and self-regulation models may describe different components of ADHD, with the working memory model more linked with the inattentive symptoms and the self-regulation model more linked to impaired inhibitory control and hyperactive/impulsive presentation. The CEM tried to explain the disorder in three distinct levels, including deficits in cognitive processes, energetic pools, and EF. While the neurodevelopmental model proposed a double dissociation model that the early onset of cADHD is associated with non-cortical dysfunction, including thalamus and brainstem, that remains static throughout the lifetime; and diminution of symptoms over development was associated with optimal development of PFC. Findings from multiple studies partially support these models. However, these existing studies have focused on individual hypotheses, without addressing the potential interactions of these theoretical models (Dias et al., Existing data clearly indicate that ADHD, as defined by the key behavior features of inattention and/or hyperactivity/impulsivity, is a highly complex and heterogeneous disorder in terms of its multi-factorial etiological risk factors, diverse neurocognitive impairments and comorbid problems. There is yet no curative treatment for ADHD. The most commonly prescribed medications for treating ADHD symptoms include methylphenidate, amphetamines, and non-stimulant drugs. In addition to medications, behavior therapy, social skills training, exercise, and nutrition management have also been implemented in practice for interventions of ADHD. Treatment responses from patients have been found to vary widely in terms of the types of optimal pharmacological treatments, dosage requirements, tolerability, response rates, and adverse-event profiles. Multiple factors, such as psychiatric and behavioral comorbidities, symptom severity levels, and inter-individual variability in the amount of dopamine released by neurons, may contribute to the treatment response heterogeneity in ADHD. Further investigations are to better understand the treatment response heterogeneity in ADHD.Heterogeneous neurobehavioral deficits in cognitive domains, such as sustained attention or vigilance, EF, working memory, and self-regulation, have been frequently reported in ADHD (Nigg et al., Nevertheless, none of the existing clinical or translational research findings have been translated into a meaning that can directly guide treatment and intervention for ADHD. Herein, it is time for future research to allocate greater resources to understanding biologically more homogeneous subgroups of ADHD, that hold the potential to facilitate the development of more tailored intervention strategies in ADHD. As reviewed above, familial history has been found to be the most significant risk factor for the emergence and persistence of ADHD (Chang et al., We also discussed possible moderators contributing to the inconsistency of the existing studies in ADHD. First of all, the sample biases may directly impact the consistency of the studies. Limited sample size, clinical heterogeneity in terms of comorbidities, gender effects, diverse courses and outcomes, varying etiological sources, also play their roles Hyman, . NeverthXL and JH designed the study. YL and DW conducted literature search and wrote the initial draft. All the authors contributed to manuscript writing and revisions.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Low birthweight (LBW) is an important predictor of neonatal and post neonatal child morality. Though its risk factors have been extensively studied in the developed world; limited epidemiological evidence is available in developing countries including Ethiopia. The purpose of the study is to determine the risk factors of LBW in North Shewa zone, Central Ethiopia.Unmatched case-control study involving 94 cases and 376 controls was conducted from Jan to Mar 2017 in three public hospitals in the zone. A case was defined as a singleton live birth with birthweight less than 2.5 kg; whereas, a control was a newborn that weighs 2.5–4.0 kg. Cases and controls were recruited on an ongoing basis until the required sample sizes were fulfilled. Data were collected by interviewing mothers, reviewing medical records and measuring the anthropometry of the mothers and the newborns. Bivariable and multivariable logistic regression analyses were used to identify risk factors of LBW. The outputs of the analyses are presented using adjusted odds ratio (AOR) with the respective 95% confidence interval (CI).Mothers with no formal education had two times increased odds of delivering LBW babies than women with formal education [AOR = 2.20 ]. Mothers with no history of nutrition counseling during pregnancy had three times increased odds of giving LBW babies than those who were counseled [AOR = 3.35 ]. Non-married women had higher odds of giving LBW newborns as compared to married ones [AOR = 3.54 ]. Mothers from food insecure households had about four times higher odds of LBW as compared to food secure mothers [AOR = 4.42 ]. In contrast to mothers who had the recommended four or more antenatal care (ANC) visits, those who were not booked had three times increased odds of giving to LBW baby [AOR = 3.03 ].Improving the socio-economic status of mothers, enhancing the utilization of ANC and strengthening the integration of nutrition counseling into ANC help to reduce LBW. According to the definition of the World Health Organization (WHO), low birthweight (LBW) is weight of an infant at birth of less than 2500 g irrespective of the gestational age . LBW canBirthweight is the single most important predictor neonatal and post-neonatal survival. It has been estimated that LBW babies are approximately 20 times more likely to die than normal babies in the first year of life . LBW babIt is estimated that globally 15to 20% of all births are LBW, representing more than 20 million births a year . HospitaLBW is caused by multifold and possibly overlapping factors that affect either the duration of pregnancy or fetal growth, or both , 3. AccoThough the risk factors of LBW have been extensively studied in the developed world; there is limited epidemiological evidence in developing countries including Ethiopia. The relative significance of the risk factors is also likely to vary across settings secondary to underlying contextual factors. Accordingly this case-control study was designed to identify factors associated with LBW in North Shewa zone hospitals, central Ethiopia.The study was carried out in three secondary-care public hospitals – Fiche, Kuyu and Dera –found in North Shewa zone, Oromia region, central Ethiopia. As of 2016, North Shewa zone had an estimated population size of 1.5 million. The zone is administratively divided in to 13 districts and has the aforementioned 3 functional hospitals, 62 health centers, 268 health posts. In 2016, the total health facility deliveries in the zone were 38,131.Unmatched case-control study with controls-to-case ratio of 4:1 was conducted from January 01 to March 30, 2017.Singleton live births in the three hospitals during the study period, irrespective of the duration of pregnancy and mode of delivery, were considered eligible for the study. Birthweight of every child was measured and newborns who weigh less than 2.5 kg were taken as cases; whereas, a similar group of children with a birthweight of 2.5 to 4.0 Kg were categorized as a controls. Multiple births, macrosomic babies (birthweight greater than 4.0 kg), mothers or newborns in critical medical conditions and babies weighed more than an hour after birth were excluded.Optimal sample size was determined via the online OpenEpi statistical program . The comThe data were collected by interviewing the mothers, reviewing medical records and measuring the anthropometry of the mothers and the newborns.Six trained midwives working in the delivery wards of the three hospitals collect the data using structured and pretested questionnaire prepared in Afan Oromo language. Eligible mothers were interviewed face to face within 24 h after delivery. Socio-demographic and economic information was assessed using standard questions extracted from the DHS questionnaire .The medical records of the mothers were reviewed and relevant information including last-normal menstrual period and ultrasound dating of pregnancy were extracted to the questionnaire.The frequency of consumption of eleven major food groups during the pregnancy was measured using a Food Frequency Questionnaire (FFQ) based on the mothers’ recall. On the other hand, the level of household food insecurity was assessed using the Household Food Insecurity Access Scale (HFIAS) of the Food and Nutrition Technical Assistance (FANTA) project. The scale categorized the subjects into four ordinal groups – secure; mild, moderate and severe insecurity .The weight of the newborns was measured within the first hour of birth using a calibrated Seca scale and rounded to the nearest 100 g. MUAC of mother was measured to the nearest 0.1 cm using MUAC tape. Anthropometric measurements were taken in duplicates by an observer and ultimately the average of the duplicates was registered.The independent variables of the study include socio-demographic factors , reproductive factors , reported illness during pregnancy), nutritional factors , work load during pregnancy and infant’s sex. The dependent variable was birthweight status dichotomized into LBW or normal birthwieght.The collected data were checked for completeness, coded and entered into Epi info version-3.5, and then exported to the Statistical Package for Social Sciences (SPSS), version 20 for analysis.Wealth index was computed as a composite indicator of living standard based on ownership of selected household assets, size of agricultural land, number of livestock owned, materials used for housing construction, and ownership of improved water and sanitation facilities. The analysis was made using the Principal Component Analysis (PCA). The generated principal component was divided into three wealth classes.The socio-demographic and other background profiles of the cases and controls were compared using chi-square test. Prior to analysis the assumptions of chi-square test were checked. When smaller expected frequencies were encountered, re-categorization of variables or merger of the levels was made.p-value less than 0.25) with the outcome variable in the bivariable models, were considered as candidate variables for the multivariable logistic regression models. In order to reduce over adjustment bias, direct and indirect predictors of LBW were fitted separately into two multivariable models [Factors associated with LBW were identified using bivariable and multivariable logistic regression models. Independent variables that demonstrated near to statistically significant association  of College of Medicine and Health Sciences, Hawassa University. Data were collected after taking informed consent from the mothers.p = 0.027). Higher proportion of the mothers of the cases (53.2%) had no formal education than that of the controls (42.0%) (p = 0.002). The two groups were significantly different in terms of ethnicity, religious affiliation and marital status (p < 0.05). However, there were no significant variations based on  household wealth index, place of residence and maternal employment status (p > 0.05)  age of the mothers of the cases was 27.4 (±6.6) years and that of mothers of the controls was 29.0(±6.4) years. About two-fifth 41.5%) of the mothers of the cases and about a quarter (27.7%) of that of the controls were in the age group 15–24 years and the difference was significant (.5% of thp = 0.001). Among mothers who had at least two births, short birth-to-birth interval (less than 2 years) was observed in 52.8% of the cases and 21.2% of the controls (p < 0.001). Among mothers of the cases, 25.5% had no ANC visits as compared to 13.8% in that of the controls (p < 0.001). Significant differences were also observed between the groups in the number of ANC consultations (p < 0.001). However, the mothers of cases and controls were not significantly different in terms of parity, presence of pregnancy related complications (p = 0.403) and infant’s sex (p = 0.781)  were preterm births than the controls (5.9%) (p = 0.019). In contrast to 42.3% of the mothers of the control babies, 28.0% of mothers of the cases had taken prenatal iron supplements (p = 0.009). About half (52.1%) of the households of the controls were food secure but the corresponding figure was significantly lower (26.6%) in the cases group (p < 0.001). The two groups were not significantly different based on reported practice of food taboo during pregnancy and prevalence of thinness (MUAC less than 210 mm)  of mothers of the cases and 58.5% of that of the controls received no nutrition counseling during the pregnancy  were considered. Among these, based on bivariable logistic regression analyses, six variables presented in Table The ultimate analysis showed the odds of LBW were increased by two fold in the mothers with no formal education as compared to their counterparts. Those who were not married had three times elevated odds of LBW as compared to married mothers. Protestant Christians had three times increased odds of LBW than Orthodox Christians Table .Table 4Sp-value less than 0.25 and hence subjected to the multivariable analysis.For the direct model, ten variables  were considered. Among them, based on bivariable analysis, five variables presented in Table The ultimate model showed that mothers who have no history of nutrition counseling during the pregnancy had more than three folds increased odds of delivering LBW babies than those who got counseling. Mothers from food insecure households had about four times higher odds of LBW as compared to food secure mothers. Taking mothers who had the recommended four or more ANC visits as reference, those who were not booked had three times increased odds of giving to LBW baby Table .This study identified socio-demographic, reproductive and nutrition related risk factors of LBW. From socio-demographic factors, absence of formal maternal education and unmarried marital status were significant predictors of LBW. Further, mothers who did not receive nutrition counseling during pregnancy and those who were from food insecure households were at increased odds of LBW.We found that mothers with no formal education had increased odds of giving to LBW newborns. This is parallel to the findings of studies conducted South-East Ethiopia , rural SBeing married was identified as a protective factor from LBW. Reasonable number of studies witnessed that the general health of married women is better than that of unmarried ones , 33. UnmThe analysis indicated that Protestant Christians had three times increased odds of LBW than Orthodox Christians. Though religious affiliation can theoretically affect dietary habits and food restrictions during pregnancy, we are not aware of the presence of such variations between the two groups in the area. The observed association possibly might have emanated from residual confounding from socioeconomic differences between the groups. Further, as the number of Protestants in the analysis was relatively small, chance could also explained the association.In this study, the odds of delivering LBW babies were higher among mothers who did not receive nutrition education during the pregnancy as compared to their counterparts. Previous studies have indicated that nutrition education and counseling during pregnancy benefits in reducing preterm and LBW births . An inteWe observed that food insecure mothers had four fold increased odds of giving birth to LBW babies than their counterparts. Intuitively, food insufficiency increases risk LBW by compromising maternal nutrient intake, pre-pregnancy weight and gestational weight gain rate. A similar pattern of association has been appreciated by studies conducted in Ethiopia and abroad , 38. ForThe results suggest that frequent ANC reduces the risk of LBW. The benefit of ANC could emanate from its various components including prevention and management of anemia and other pregnancy complications. Previous studies came up with conflicting findings. A systematic review concluded that prenantal care prevents neither preterm birth nor IUGR . On the The findings of the study should be interpreted in consideration of the following methodological shortcomings. As the study employed a retrospective design, recall errors in the measurement of exposures (e.g. frequency of consumption of different food groups or events related to household food insecurity) are possible. Such errors are likely to produce misclassification of exposures and ultimately may underestimate the strength of associations. Further, as any observational study, residual confounding from unmeasured or misclassified variables cannot be ruled out.For some of the potential risk factors including gestational age, practice of food taboo and occurrence of pregnancy complications, the observed frequencies were low and this might have reduced the statistical power to detect actual differences. Further, due to the case-control nature of the study, we did not measure some potentially predictors of LBW including prepregnancy weight, gestational weight gain and paternal anthropometry. As study was restricted to hospitals the findings may not be fully generalizable to the entire births in the locality.The study demonstrated that maternal educational status, marital status, exposure to nutrition counseling during pregnancy and household food insecurity were significant predictors of LBW. Improving the socio-economic status of mothers, expansion of the utilization of ANC and strengthening the integration of nutrition counseling into ANC help to reduce LBW.


Attention-deficit/hyperactivity disorder (ADHD) is often comorbid with other psychiatric conditions in adults. Yet, less is known about its relationship with common metabolic disorders and how sex and ageing affect the overall comorbidity patterns of adult ADHD. We aimed to examine associations of adult ADHD with several common psychiatric and metabolic conditions. Through the linkage of multiple Swedish national registers, 5,551,807 adults aged 18 to 64 years and living in Sweden on December 31, 2013 were identified and assessed for clinical diagnoses of adult ADHD, substance use disorder (SUD), depression, bipolar disorder, anxiety, type 2 diabetes mellitus (T2DM), and hypertension. Logistic regression models and regression standardization method were employed to obtain estimates of prevalence, prevalence difference (PD), and prevalence ratio (PR). All comorbid conditions of interest were more prevalent in adults with ADHD (3.90% to 44.65%) than in those without (0.72% to 4.89%), with the estimated PRs being over nine for psychiatric conditions (p < 0.001) and around two for metabolic conditions (p < 0.001). Sex differences in the prevalence of comorbidities were observed among adults with ADHD. Effect modification by sex was detected on the additive scale and/or multiplicative scale for the associations of adult ADHD with all comorbidities. ADHD remained associated with all comorbidities in older adults aged 50 to 64 when all conditions were assessed from age 50 onwards. The comorbidity patterns of adult ADHD underscore the severity and clinical complexity of the disorder. Clinicians should remain vigilant for a wide range of psychiatric and metabolic problems in ADHD affected adults of all ages and both sexes. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset neuropsychiatric disorder that often persists into later life . The preClinical studies have documented that adults with ADHD tend to suffer from psychiatric disorders such as substance use disorder (SUD), depression, bipolar disorder, and anxiety , 5. MostIn this population-based cross-sectional study, we sought to provide a broader perspective on the comorbidity patterns of adult ADHD by estimating associations of adult ADHD with several common psychiatric and metabolic conditions, using data from the Swedish national registers. Specifically, we aimed to replicate previously reported associations of adult ADHD with psychiatric conditions, including SUD, depression, bipolar disorder, and anxiety, and explore associations with metabolic conditions, in particular T2DM and hypertension. We then assessed sex differences in these associations, and examined the comorbidity patterns of adult ADHD in individuals aged 50 to 64 years.All residents in Sweden are assigned a unique personal identity number that allows for large-scale linkage across the Swedish national registers . The TotThe study population consisted of 5,551,807 adults aged 18 to 64 years and living in Sweden on December 31, 2013, who were identified from the Swedish Total Population Register. A subgroup of 1,665,938 adults aged 50 to 64 were selected for exploring the comorbidity patterns of adult ADHD in older adults.Data on diagnoses of adult ADHD and comorbidities under study were retrieved from the Swedish National Patient Register . AscertaFirst, we estimated prevalence of each comorbid condition in adults with and without ADHD, as well as the corresponding prevalence ratio (PR), using logistic regression models and regression standardization method . The modPij is the prevalence of a comorbid condition in the stratum of sex i and adult ADHD status j, with i = 0 denoting male, i = 1 denoting female, j = 0 denoting absence of adult ADHD, j = 1 denoting presence of adult ADHD. Essentially, difference in PD reflects whether the joint effect of both female sex and adult ADHD on the prevalence difference scale is different (difference in PD ≠ 0) from the sum of the effects of female sex alone and adult ADHD alone. Difference in PD = 0 indicates no effect modification by sex on the additive PD scale. Next, we estimated sex-stratified PR for each comorbid condition, comparing adults with ADHD to those without. To evaluate whether the associations were modified by sex on the multiplicative PR scale, we calculated ratio of PR in females to PR in males for the comorbid condition asSecond, we tested the presence of effect measure modification (hereinafter referred to as effect modification) by sex on the observed associations. We began with estimating sex-stratified PD for each comorbid condition, comparing adults with ADHD to those without. PD thus reflects the absolute prevalence increase of a comorbid condition associated with ADHD. To assess effect modification by sex on the additive PD scale for the associations, we calculated difference in PD between females and males asRatio of PRs reflects whether the joint effect of female sex and adult ADHD on the PR scale is different (ratio of PRs ≠ 1) from the product of the effects of female sex alone and adult ADHD alone. Ratio of PRs = 1 indicates no effect modification by sex on the multiplicative PR scale. Despite assessment of effect modification by sex on two selected scales in the current study, other scales might be of biological importance as well. For an association of interest, effect modification by sex could be present on any scale but not necessarily all. Nevertheless, assessing additive effect modification is considered important for identifying a target group for intervention and thus more relevant to public health decision-making . LogistiFinally, we explored the associations among older adults aged 50 to 64, with diagnoses of all conditions being assessed from age 50 onwards. The analyses were adjusted for sex and age in years.Robust standard errors were calculated to construct 95% confidence intervals (CIs), accounting for data clustering within the same families. The significance level for all tests was set at p < .05, two-tailed. SAS software version 9.4  was used for data mining. R software version 3.4.1  was usedThe study population comprised 5,551,807 adults  aged 18 to 64 years (Mean [SD] age: 40.55 [13.49] years) and living in Sweden on December 31, 2013, of whom 61,129 (1.10%) were diagnosed with ADHD at some point in their adult life. More descriptive information on the distribution of sex, age, and number of psychiatric and metabolic conditions by adult ADHD status is shown in The prevalence estimates of SUD (35.12%), depression (42.28%), and anxiety (44.65%) were more than nine times higher in adults with ADHD than in those without . BipolarAmong adults with ADHD, depression, bipolar disorder, and anxiety were more prevalent in females than in males, whereas SUD, T2DM, and hypertension were more prevalent in males than in females . The mosWe observed additive modifying effects of sex on the associations of adult ADHD with all comorbidities except T2DM . For depMultiplicative effect modification by sex was detected for the associations of adult ADHD with all comorbidities except hypertension . FemalesWhen diagnoses of adult ADHD and comorbidities under study were assessed from age 50 onwards, the prevalence of ADHD was 0.29% in adults aged 50 to 64. The associations of adult ADHD with all comorbidities remained statistically significant .This large population-based investigation of common psychiatric and metabolic comorbidity of adult ADHD generated three main findings. First, SUD, depression, bipolar disorder, anxiety, T2DM, and hypertension were more prevalent in adults with clinically diagnosed ADHD than in those without. Second, effect modification by sex was detected on the additive and/or multiplicative scales for the associations of adult ADHD with all comorbidities. Finally, ADHD remained associated with all comorbidities in adults aged 50 to 64, when all conditions were assessed from age 50 onwards.Our findings reinforce the associations of adult ADHD with SUD, depression, bipolar disorder, and anxiety found in prior studies , 21, 22.Our findings about associations of clinically diagnosed ADHD with T2DM and hypertension might reflect health-risk behaviors among adult patients with comorbid ADHD. As others have noted , inattenIn contrast to our findings, the prevalence estimates of hypertension or other cardiovascular diseases were similar between adults with ADHD and controls in a study based on U.S. health care claims . The disAmong adults with ADHD, we found that the prevalence estimates of depression, bipolar disorder, and anxiety were higher in females than in males, whereas the prevalence estimates of SUD, T2DM, and hypertension were higher in males than in females. The top three comorbidities of adult ADHD were all psychiatric conditions, namely, SUD, depression, and anxiety, with the most common condition being anxiety in females and SUD in males. T2DM is the least common comorbidity in both females and males with ADHD. Unlike prior studies showing no evident sex differences in associations between adult ADHD and psychiatric comorbidities , 10, we Our findings about comorbidity of ADHD in older adults (50 to 64 years) provide a large-scale corroboration of previous findings from a few available studies on this topic. One small study of adults aged 60 to 94 years showed excess risk of depression and anxiety symptoms in those with ADHD compared to those without . AnotherIt should be acknowledged that the current study was subject to certain limitations. First, ADHD was generally under-diagnosed in adults and largely overlooked in the older group (aged 45 to 64 years by the end of 2013) primarily because ADHD as a diagnostic category was rarely considered for adults in Sweden before 2002. Patients treated in primary care or failing to seek medical care were not covered by the National Patient Register. Consequently, the study could not avoid a certain amount of false negatives, giving rise to an underestimation of the associations. Second, all conditions under study were assessed via registered ICD diagnoses. Although differences in ICDs could lead to different estimates of point prevalence, patients diagnosed with these conditions tended to represent relatively severe cases and have more frequent visits to clinicians, which might induce detection bias and overestimation of the associations. Despite the first and second limitations, the true associations are still likely to be statistically significant given the magnitude of the estimates. Third, although ADHD is a persistent condition, its onset by diagnostic criteria typically precedes the onset of the comorbidities. Further, individuals who received the first diagnosis of ADHD in adulthood are likely to be those who were undiagnosed and untreated in childhood. However, due to a lack of data on outpatient specialty care before 2001 and primary care, we could not determine whether individuals with a diagnosis of ADHD in adulthood had ever been diagnosed in childhood, or deduce the extent to which the development of comorbidities was related to delay in detection and management of childhood ADHD symptoms. Neither could we infer the temporal order of the onsets of the comorbidities. The intriguing question deserves thorough investigation in future longitudinal research. Fourth, in the current study, 83.5% of all adults with an ADHD diagnosis in the National Patient Register had at least one prescription of ADHD medication. However, the Prescribed Drug Register was first introduced in July 2005, which makes it difficult to infer whether the initiation of pharmacological treatment for ADHD preceded the first diagnoses of comorbid conditions. In addition, information on non-pharmacological treatment is not available in the registers. Future research should therefore address the role of pharmacological as well as non-pharmacological treatment for ADHD using appropriate study designs.In conclusion, the comorbidity patterns of adult ADHD underscore the severity and clinical complexity of the disorder. Clinicians should remain vigilant for a wide range of psychiatric and metabolic problems in ADHD affected adults of all ages and both sexes.S1 Table(DOCX)Click here for additional data file.


Attention-deficit hyperactivity disorder (ADHD) and head injury (including traumatic brain injury (TBI)) manifest in high levels across prison samples and guidance from the National Institute for Health and Care Excellence notes that people with acquired brain injury may have increased prevalence of ADHD. We aimed to examine the association of ADHD with TBI and the impact of the association upon health-related quality of life (HRQoL) and service use among imprisoned adults.An observational study was performed in 2011–2013, at Porterfield Prison, Inverness, United Kingdom (UK). The all male sample included 390 adult prison inmates with capacity to consent and no history of moderate or severe intellectual disability. Head injury was measured with a series of self-reported questions, addressing history of hits to the head: frequency, severity, loss of consciousness (LOC), and sequelae. Participants were interviewed using the Diagnostic Interview for ADHD in Adults 2.0. The Health Utilities Index Mark 3 was used to measure health status, and to calculate attribute specific HRQoL and Quality-Adjusted Life Year (QALY) scores.72% of prisoners sampled reported at least one head injury in their lifetime. Among those, 70% of head injuries occurred before age 16 and 70% experienced LOC. Prisoners with ADHD were nearly twice more likely to have TBI. Prisoners with ADHD-only and ADHD with co-morbid TBI had significantly lower scores in several HRQoL attributes, compared with TBI only or the absence of either condition. Adjusted logistic regression models indicated an average reduction of 0.20 QALYs in inmates with ADHD-only and 0.30 QALY loss in those with ADHD with co-morbid TBI compared with inmates with neither condition.There is a robust association between ADHD and TBI, and ADHD with co-morbid TBI confers significantly greater impairment in terms of HRQoL. Managing the short and long-term consequences of TBI is essential to improving care for prisoners and to addressing the criminogenic factors related to them. Despite the high prevalence of both attention-deficit hyperactivity disorder ADHD; 26%)  and head6%  and hp < 0.0001), with accidents being the leading cause of death [ADHD is a childhood onset neurodevelopmental disorder, characterised by a persistent pattern of inattention and/or hyperactivity-impulsivity . ADHD isof death .Prevalence rates for head injury and TBI vary depending on the source of information, definition, the occurrence of loss of consciousness (LOC), and/or loss of memory. A meta-analysis indicated that 60% of the overall offender population is reported to have TBI with LOC , which iThe possible association between ADHD and head injuries is complex. Difficulties with the ability to self-regulate have been described as an executive function that is central to ADHD symptoms , which mIn considering impairments and functional states that may be affected by disease, injury, and treatment, the Health Utilities Index Mark 3 (HUI3) is an effective tool to evaluate overall health status and quality of life outcomes , 26. ResTo the best of our knowledge this is the first study addressing the health status of adult prison inmates with ADHD and TBI using the HUI3. We hypothesize that: 1) there is a significant association between ADHD and TBI amongst prison inmates, and 2) prisoners with ADHD and co-morbid TBI have an increased likelihood of adverse health, intellectual ability, and quality of life outcomes, compared with those with ADHD only, TBI only, or neither.Following approval from the Scottish Prison Service Research Access and Ethics Committee and in accordance with the Declaration of Helsinki, prisoners were recruited by opportunity sampling from Porterfield Prison, Scotland, UK, over a period of 18 months in 2011–2013. Participants included 390 adult male prisoners who consented to participate. Those with moderate or severe learning difficulties, lack of fluency in the English language, and severe mental illness were excluded from participating.Participants in the study were indirectly compensated. The study group deposited £20 per participant into a Prison Common Good Fund, which was managed by a group of prisoners. The fund was then used to purchase items for the common good of all prisoners to enhance prison life.2 (df 1) = 8.20. p < 0.01).Prisoners who indicated interest attended an appointment with the researcher where they were given detailed  information about the study and consent procedures. After obtaining consent, two psychology researcher assistants administered a comprehensive battery of measures, which took approximately 4 h to complete . The researchers received comprehensive training to administer the Diagnostic Interview for ADHD in Adults 2.0 (DIVA-2) from the Maudsley Hospital Adult ADHD Service, which included observations and training on reaching diagnostic classification to ensure inter-rater reliability . The resHead injury was measured via a self-reported questionnaire addressing history of hits to the head. The key question “Have you ever been hit in the head?” was followed by further questions detailing the injury and its consequences including: frequency, age of onset, occurrence of LOC, cause, related medical service use, and occurrence of related memory loss.Definitions of TBI are often based on the occurrence of any LOC and on the extent of post-traumatic amnesia (PTA). Considering that head injury incidents were self-reported by prisoners, we aimed to make the identification of a likely TBI more robust. Recording the number of head injuries increased the threshold level and minimised false positives, and also helped to distinguish those most likely to have had any form of sequelae after their injuries.andAt least one head injury andOccurrence of LOC orAny additional indicator of head injury severity  Medical service utilisation related to the head injury (such as a visit to the doctor or Accident and Emergency (A&E) services).We determined the lifetime prevalence of a likely TBI based upon the occurrence of the following:Because head injury history was not entered into prisoners’ medical records, we were unable to formally apply the commonly used Mayo Classification System (MCS) . But, byhttp://www.healthutilities.com and review the annotated references of articles from hundreds of studies worldwide for evidence of HUI validity , reliability and responsiveness.”The HUI3 is a multi-attribute health status classification system that enables researchers to map levels of: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain; using decision tables and coding algorithms, which can be represented in terms of attribute specific HRQoL scores . The HRQPrisoners’ were asked to answer HUI3 questions based upon their health status in the 4 weeks prior to the interview. The HUI3 composite score was used to calculate QALYs and was extrapolated to one year to represent the study health evaluation time frame.www.eunetworkadultadhd.com/validation-of-diva-2-0/). Questions addressed their current and childhood (ages 5 to 12) presentation of ADHD symptoms and scope of impairment.All participants underwent a comprehensive evaluation for ADHD and were interviewed using the Diagnostic Interview for ADHD in Adults 2.0 (DIVA-2) . The DIVAll participants were screened for conduct disorder using the CD scale of the Barkley Adult ADHD Rating Scale-IV (BAARS-IV) , which cMedical service utilization was obtained through inspection of prison medical records. Researchers recorded service use over the three months prior to the interview date, such as: visits to medical doctor, visits to accidents and emergency room (A&E), admission to hospital and length of stay, and surgeries. The authors chose to include three months of service for practical reasons; and additionally felt this time period fairly represented the service use of all prisoners given the variance in prison stays.IQ was estimated using the vocabulary subtest of the Wechsler Abbreviated Scale of Intelligence (WASI) . The voct-tests were used to contrast age and age of first injury by groups. Logistic regression models were fitted using ADHD as the exposure for any head injury, TBI, causes and consequences of head injury, and service utilisation. Ordered logistic regression was used to analyze number of head injuries reported.Frequencies were reported for all categorical variables, and means (with standard deviations) for continuous variables. Student Odds ratios (OR) were reported as indicators of the magnitude of associations in binary (e.g. presence or absence of head injury) and on the ordered categorical outcome (head injuries reported) models. Logistic regression analyses were repeated exclusively on those who reported their first injury before age 16. This analysis allowed us to examine whether a TBI occurring before age 16 may be associated with ADHD. We used age 16 because it represented the threshold for legal adulthood in the UK.Tobit regressions were used in favour of traditional ordinary least-squares (OLS) regression models to estimate the relationship between inmate groups of ADHD only, TBI only, ADHD with co-morbid TBI, and groups having neither (reference group); and HUI3 HRQoL and QALY scores. The Tobit model is designed to estimate linear relationships between variables when there are ceiling or flooring effects on the outcome [ outcome . In our p ≤ 0.05 for all statistical tests. All analyses were performed using Stata version 13 (StataCorp) [All models were adjusted for age at time of survey and for the presence of CD whenever necessary. We established the significance level at ataCorp) .The all male sample was essentially Caucasian British (99.0%) with an average age of 30.3 years (sd 8.3). Mean ages for each of the groups were: 28.6 years (sd 7.9) for ADHD only, 32.3 years (sd 8.4) for TBI only, 27.9 years (sd 7.2) for ADHD with co-morbid TBI, and 30.2 years (sd 8.5) for neither ADHD nor TBI. Bonferroni post hoc analyses indicated a significantly younger age for the ADHD with co-morbid TBI group contrasted against the TBI only group.Two hundred eighty participants 71.8%) reported having experienced at least one head injury in their lifetime, and 169 (43.3%) were considered to have a likely TBI according to our strict definition  services, and 19.3% required surgery. Additionally, 23% reported experiencing either anterograde or retrograde memory loss. 44% of the head injuries were due to an accident, and 56% were the consequence of a violent assault (e.g. hit with bat) (see Table p = 0.07). Among prisoners reporting at least one head injury, prisoners with ADHD did not differ from those without ADHD in terms of cause, LOC, or sequelae  , with nearly twice the odds among those with ADHD to have experienced a TBI . Notably, a significantly high proportion (82%) of those with ADHD and at least one head injury reported their first injury occurring before age 16 , p < 0.001) , 2 (df 3) = 43.6 p < 0.000). The rate was highest among the ADHD/TBI group (69.8%), followed by ADHD only (65.1%). The rate for TBI only was 29.3% and similar to the rate of 30.3% for none of the ADHD, TBI or combined conditions being met.Out of the total sample of 390, 153 (39.2%) met screening criteria for CD. There was a significant difference in CD screening diagnosis across the four groups: ADHD only; TBI only; ADHD/TBI; and remaining participants  and cognition (p < 0.01) attributes. Those with ADHD with co-morbid TBI had the most impaired specific health attributes with significantly poorer scores on vision (p < 0.001), ambulation (p < 0.05), emotion (p < 0.001), cognition (p < 0.01) and pain (p < 0.05) attributes. Prisoners without ADHD or likely TBI had an average QALY of 0.72 (sd 0.26) and both the ADHD-only and ADHD with co-morbid TBI groups had significantly greater loss  than those with neither.Prisoners with ADHD-only had significantly poorer utility scores on emotion (p < 0.01) and addiction services (p < 0.05) compared with those with neither. Adjusted models for IQ demonstrate that prisoners with ADHD with co-morbid TBI had significantly lower intelligence scores compared to those with neither (p < 0.01)  Table . BecauseIn the present study we set out to examine the potential association of ADHD with TBI in a UK prison sample and the impact that each has on health-related quality of life. Our hypotheses were supported. Our results indicate that prisoners with a clinical diagnosis of ADHD were significantly associated with our strict definition of a likely TBI; 55% were almost twice more likely to have TBI. Furthermore, our results indicate a high frequency of head injury at an early age; 82% of prisoners with ADHD with at least one head injury reported that their first injury occurred before age 16. In addition, the risk for adverse health, intellectual ability, and quality of life outcomes, appears greater among prisoners having ADHD with co-morbid TBI than for those with ADHD-only, TBI-only, or neither.Our results are not consistent with another study addressing head injury among youth offenders . There tOur results are consistent, however, with findings from a recent prison study examining externalizing symptoms and injury severity . IncreasOur results are also consistent with reports from large cohort studies at the population level. Researchers have found evidence of a prospective association between head injury before age 2 and a later diagnosis of ADHD . NonethePrisoners in our study without ADHD or likely TBI had an average QALY of 0.72, which was notably lower than estimates for the general population. Using the HUI-3 and EQ-5D, general population norms in the US and UK estimate an average age-relevant QALY of 0.93 and 0.909, respectively , 37. We Analyses of specific HRQoL attributes demonstrated the types and extent of impairments. Our finding that prisoners with ADHD had significant impairments related to emotion and cognition, were to be expected, and may likely reflect the influence of co-morbid anxiety or mood disorder . Our finThe significantly poorer vision score among the ADHD with TBI group may relate to their reading difficulties. In the present study, out of the 53 participants in the ADHD/TBI group, 22 (41.5%) required assistance with reading the questionnaires in comparison with only 17.5% of the other participants. With respect to mobility, the finding that prisoners with ADHD/TBI have significantly poorer ambulation problems may reflect that prisoners with the combined condition suffer more injuries that hinder their mobility compared with other prisoners. For example, data obtained from the Danish registry has reported that the morbidity rate is nearly three times higher if you have ADHD, and that 77.8% of unnatural deaths are accounted for by accidental injury .To the best of our knowledge, this is the first study addressing the health status of adult prison inmates with ADHD and TBI using the HUI3. A previous study of children with ADHD reported that higher symptom dimensions of inattention, hyperactivity and impulsivity correlated with poorer HRQoL, along with conduct problems, physical complaints, and somatic symptoms . AdditioA key strength of the study is its large sample size and a methodology in which every participant was clinically diagnosed using the DIVA-2. Nonetheless, there are several limitations.The cross-sectional design of the study limits any causal interpretation of the association between TBI and ADHD, and other response variables.Definitions of TBI often vary depending on the source of information, occurrence of LOC and/or loss of memory, and because of this, many results in studies of TBI cannot be generalized. For example: study participants had to rely on their own or a family member’s recollection of their trauma events. Recall bias is unaccounted for and may have been a factor on all reported measures of head injury, health status, and service use.Another limitation is that our findings may not be applicable and generalized beyond the prison population and will likely require replication using other clinical samples. Furthermore, our study results may not be applicable and generalized to female or ethnic minority groups of prisoners. Females often present as clinically distinct from males in correctional services, as they have been found to have the potential for greater impairment in ADHD symptomatology and substance dependence rates .Finally, there is an absence of data in our study on co-morbid factors associated with ADHD and TBI, such as conduct disorder, anxiety, mood disorders, substance misuse, antisocial behaviours, and aggression –46, whicBoth ADHD and TBI manifest in high proportions across prison samples. We provide evidence of a strong direct association between them, and evidence to suggest that prisoners with ADHD with co-morbid TBI present with significant impairment in several aspects of HRQoL. Prison inmates with ADHD and those with ADHD with co-morbid TBI had reduced 0.20 and 0.30 QALY respectively when compared with inmates with neither.The comprehensive health screening tool (CHAT) is an effective screen for both ADHD and TBI in youth offender institutions . The bri


N = 1828 youth assessments and N = 998 parent assessments from N = 447 unique families. Overweight was defined as a body mass index (BMI) ≥ 85th percentile for youth of the same age and sex; overweight in adults as a BMI ≥ 25. Effects of age, gender, and medication use  were taken into account. Generalized estimation equations were used to correct for within-family and within-subject correlations. There was no difference in risk between ADHD-affected youth and their unaffected siblings . However, compared to population prevalence data, all ADHD family members alike were at increased risk for being overweight: ADHD-affected youth , unaffected siblings , mothers  and fathers . Parental overweight—but not parental ADHD—was predictive of offspring overweight . Being overweight runs in ADHD families, yet is not specifically linked to ADHD within families. Shared unhealthy lifestyle factors  as well as genetic factors shared by family members likely explain the findings.The widely reported association between ADHD and overweight may be attributable to genetic and environmental factors also present in unaffected family members. Therefore, the purpose of this study was to examine the association between ADHD and overweight within families. A cohort was used of families with at least one member with ADHD, recruited as part of the Dutch node of the International Multicenter ADHD Genetics (IMAGE) study, with assessments taking place between 2003 and 2006, 2009 and 2012, and 2013 and 2015. The three assessment waves yielded The online version of this article (10.1007/s00787-019-01331-7) contains supplementary material, which is available to authorized users. Is there a relationship between attention-deficit/hyperactivity disorder (ADHD) and being overweight? This has been the central question for many small- and large-scale studies during the last 2 decades. Two recent meta-analyses concluded that people with ADHD have a 20–30% increased chance of being overweight compared to population prevalence rates , 2. IncoVarious mechanisms have been proposed to explain the association between ADHD and overweight. ADHD may causally lead to overweight through impulsive (binge) eating  since ADN = 11,159), the association between children’s (6–17 years) ADHD symptoms and body mass index (BMI) status was strongly attenuated when parental BMI was accounted for [To test this model, this study employs a within-family design to examine mechanisms underlying the association between ADHD and being overweight in ADHD-affected and -unaffected family members taking into account age, gender, and medication use since these have been reported as moderating factors –28. A fanted for . This maN = 369) were recruited as part of the Dutch node of the International Multicenter ADHD Genetics (IMAGE) study, with assessments taking place between 2003 and 2006 (wave 1), see Figure S1. All families were invited for a second assessment (wave 2), which took place between 2009 and 2012. Follow-up rate was 75.9% (N = 280 families). New families (N = 45) were recruited at the second assessment wave as well [N = 184) was invited for the third assessment  with a follow-up rate of 47.3% (N = 87). Again, new families (N = 33) were added to the sample. The three assessment waves yielded N = 1828 youth assessments and N = 998 parent assessments from N = 447 unique families, for a description of the full sample see Table N = 361 assessments from N = 136 unique families.Families , hyperactivity/impulsivity (M), and total symptoms (N) were considered clinical. Youth scoring clinically on any of these subscales were administered a semi-structured, standardized, investigator-based interview: the parental account of children’s symptoms (PACS) (wave 1) or the schedule for affective disorders and schizophrenia for school-age children—present and lifetime version (K-SADS [Conner’s long-version parent and teacher questionnaires (youth < 18 years) or parent and self-report (youth ≥ 18 years) were used to screen all youth –33. T sc .Self-reported ADHD symptoms in the parents were measured with either the ADHD rating scale (wave 1)  or the CBody weight and height were measured by professionals in the hospital. The individuals were clothed but wore no shoes or coats. Weight was measured to 0.1 kg using an electronic flat scale , height was determined to the nearest 5 mm using a wall-mounted tape measure . BMI was calculated as weight in kilograms divided by squared height in meters . The claFull-scale IQ of all youth was estimated by four subtests  of the WISC/WAIS-III  .For ADHD-affected offspring, we recorded the use of psychostimulants (methylphenidate and atomoxetine), antipsychotics (risperidone), and melatonin up to 4 months prior to the assessment day. Preferably, pharmacy transcripts were used to obtain data on medication use (yes/no). If pharmacy records were not available, self-report questionnaires on current and past medication use were used as binary measures.Socio-economic status was operationalized by the highest successfully completed education of each parent. This scale contained nine levels, ranging from 0  to 9 (university education) , 43.2 (continuous), and IQ (continuous). In the second step (extended youth model), two-level interactions among predictors were added to the default model predicting youth overweight status (yes/no); predictors were dropped in case of non-significance. In a third step , the following variables were added to the extended model predicting youth overweight status (yes/no): age parent (continuous), education parent (continuous), ADHD score parent above cut-off (yes/no), and overweight status parent (yes/no). Parental data were only available for a subsample of 539 individuals (N = 828 assessments). In the last step (affected youth medication model), for ADHD-affected youth, the extended model predicting youth overweight status (yes/no) was extended by adding the following predictors: use of psychostimulants (yes/no), antipsychotics (yes/no), and melatonin (yes/no). Two-level interactions among predictors were added to the medication model and dropped in case of non-significance.Analyses were performed using the Statistical Package for Social Sciences, version 22.0 for Windows . Generalized estimating equations (GEE) were used to correct for within-family and within-subject correlations (correlated measurements over time). In the first step (default youth model), we predicted youth overweight status (yes/no) by ADHD diagnosis (yes/no), gender , age (continuous), age2 (continuous) as predictors (default parent model). Two-level interactions among predictors were added to the default model and dropped in case of non-significance (extended parent model).Parental overweight was predicted using ADHD above cut-off (yes/no), gender , age (continuous), and ageContinuous variables were centered to the median, enabling predictive values to reflect the median population . All final probability models were created by backward elimination with a significance level of 0.1. Only for two essential youth variables (IQ and BMI), multiple imputation (20 imputations) was used for missing values (< 15%).Estimates of the extended youth and parent probability models were used to plot overweight probabilities stratified by gender , family member , and age (continuous). Youth and adult population prevalence were included in these plots for reference. For youth, these plots ranged till the age of 21 years since population prevalence data were available for youth till the age of 21 years. Odds ratios were calculated to compare the prevalence of overweight in ADHD-affected family members by the overweight population prevalence. Comparisons to the control family cohort are presented in the supplementary material. Correction for multiple testing was applied using the false discovery rate (FDR) .Demographics of the sample are shown in Table In relation to the main objective whether or not ADHD is associated with overweight within families, the first step (default youth model) showed no main effect of ADHD status on overweight status , i.e., youth with and without ADHD were equally likely to be overweight. In the second step (extended youth model), we found a small ADHD status by age interaction effect such that the risk for being overweight increased with age in youth with ADHD but not in unaffected youth ; however, this effect did not survive correction for multiple testing. No other significant or near-significant two-level interactions among predictors were found in the extended model.2, and IQ). Both maternal and paternal overweight predicted overweight in their offspring  , adding parental variables to the model predicting youth overweight status did not change the predictive value of the default model variables  was predicted by gender but not age: fathers were found to be more often overweight than mothers  were fully in line with the absence of an association between ADHD and overweight in youth using thorough diagnostic procedures for ADHD. Also, several predictors  as well as the outcome measure (overweight) were dichotomized for the purpose of potentially having a clear-cut message based on clinically meaningful variables. Dichotomous predictors and outcome may reduce the chances of finding more subtle effects due to loss of information, yet the large sample size makes it unlikely that this explains the absence of an association between ADHD and being overweight within families. Thereby, comorbid conditions (like depression) associated with overweight were not included in the analyses. However, this does not alter the absence of an association between ADHD and overweight within families.In conclusion, our results indicate that being overweight runs in ADHD families, yet is not specifically linked to ADHD within these families. Inadequate health behaviors such as poor nutritional habits, lack of physical activity, or chronic stress as well as genetic factors shared by family members likely explain the findings. Gene × environment interaction studies including inadequate health behaviors and chronic stress on mental and physical health should be an important research priority. Given the profound negative health consequences of being overweight, more attention to intervention strategies targeting inadequate health behaviors in families of children with ADHD is a clinically important issue.Supplementary material 1 (EPS 5654 kb)Supplementary material 2 (DOC 45 kb)Below is the link to the electronic supplementary material.