Comorbid depressive disorders in ADHD. the role of ADHD severity, subtypes and familial psychiatric disorders

ObjectiveaaTo evaluate the presence of Major Depressive Disorder (MDD) and Dysthymic Disorder (DD) in a sample of Italian children with Attention Deficit Hyperactivity Disorder (ADHD) and to explore specific features of comorbid depressive disorders in ADHD. MethodsaaThree hundred and sixty-six consecutive, drug-naïve Caucasian Italian outpatients with ADHD were recruited and comorbid disorders were evaluated using DSM-IV-TR criteria. To evaluate ADHD severity, parents of all children filled out the ADHD Rating Scale. Thirty-seven children with comorbid MDD or DD were compared with 118 children with comorbid conduct disorder and 122 without comorbidity for age, sex, IQ level, family psychiatric history, and ADHD subtypes and severity. Resultsaa42 of the ADHD children displayed comorbid depressive disorders: 16 exhibited MDD, 21 DD, and 5 both MDD and DD. The frequency of hyperactive-impulsive subtypes was significantly lower in ADHD children with depressive disorders, than in those without any comorbidity. ADHD children with depressive disorders showed a higher number of familial psychiatric disorders and higher score in the Inattentive scale of the ADHD Rating Scale, than children without any comorbidity. No differences were found for age, sex and IQ level between the three groups. Conclusions: Consistent with previous studies in other countries, depressive disorders affect a significant proportion of ADHD children in Italy. Patient assessment and subsequent treatment should take into consideration the possible presence of this comorbidity, which could specifically increase the severity of ADHD attention problems

Development and assessment of a new framework for disease surveillance, prediction, and risk adjustment: the diagnostic items classification system

IMPORTANCE: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: To develop an ICD-10-CM-based classification framework for predicting diverse health care payment, quality, and performance outcomes. DESIGN SETTING AND PARTICIPANTS: Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using R 2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. MAIN OUTCOMES AND MEASURES: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. RESULTS: A total of 65 901 460 person-years were split into 90$5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R 2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. CONCLUSIONS: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.Published versio

The Brief Negative Symptom Scale (BNSS): Independent validation in a large sample of Italian patients with schizophrenia

BACKGROUND: The Brief Negative Symptom Scale (BNSS) was developed to address the main limitations of the existing scales for the assessment of negative symptoms of schizophrenia. The initial validation of the scale by the group involved in its development demonstrated good convergent and discriminant validity, and a factor structure confirming the two domains of negative symptoms (reduced emotional/verbal expression and anhedonia/asociality/avolition). However, only relatively small samples of patients with schizophrenia were investigated. Further independent validation in large clinical samples might be instrumental to the broad diffusion of the scale in clinical research. METHODS: The present study aimed to examine the BNSS inter-rater reliability, convergent/discriminant validity and factor structure in a large Italian sample of outpatients with schizophrenia. RESULTS: Our results confirmed the excellent inter-rater reliability of the BNSS (the intraclass correlation coefficient ranged from 0.81 to 0.98 for individual items and was 0.98 for the total score). The convergent validity measures had r values from 0.62 to 0.77, while the divergent validity measures had r values from 0.20 to 0.28 in the main sample (n=912) and in a subsample without clinically significant levels of depression and extrapyramidal symptoms (n=496). The BNSS factor structure was supported in both groups. CONCLUSIONS: The study confirms that the BNSS is a promising measure for quantifying negative symptoms of schizophrenia in large multicenter clinical studies

Prospective Study on the Association between Harm Avoidance and Postpartum Depressive State in a Maternal Cohort of Japanese Women

BACKGROUND: Recent studies have displayed increased interest in examining the relationship between personality traits and the onset, treatment response patterns, and relapse of depression. This study aimed to examine whether or not harm avoidance (HA) was a risk factor for postpartum depression measured by the Edinburgh Postnatal Depression Scale (EPDS) and the state dependency of HA. METHODS: Pregnant women (n=460; mean age 31.9±4.2 years) who participated in a prenatal program completed the EPDS as a measure of depressive state and the Temperament and Character Inventory (TCI) as a measure of HA during three periods: early pregnancy (T1), late pregnancy (around 36 weeks), and 1 month postpartum (T2). Changes in EPDS and HA scores from T1 to T2 were compared between the non depressive (ND) group and the postpartum depressive (PD) group. RESULTS: There was no significant difference in the level of HA between the ND and PD groups at T1. In the ND group, EPDS and HA scores did not change significantly from T1 to T2. In the PD group, both scores increased significantly from T1 to T2 (EPDS, p<0.0001; HA, p<0.048). In the ND and PD groups, a significant positive correlation was observed in changes in EPDS and HA scores from T1 to T2 (r=0.31, p=0.002). CONCLUSIONS: These results suggest that HA cannot be considered a risk factor for the development of postpartum depression measured by EPDS. Furthermore, HA may be state dependent

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life