scholarly journals The Caregiver Reported Autistic Symptoms in Preschool Children: Findings of Chandigarh Autism Screening Instrument (CASI) Linked Screening from North India

2020 ◽  
Author(s):  
Jaison Joseph ◽  
Komal Hooda ◽  
Indu Chauhan ◽  
Komal Dhull
Keyword(s):  
Socioeconomic Status ◽  
Preschool Children ◽  
Neurodevelopmental Disorder ◽  
Communication Disorders ◽  
Screening Instrument ◽  
Screening Tools ◽  
North India ◽  
Cutoff Score ◽  
Autism Screening ◽  
Autistic Symptoms

Abstract Background Autism is a neurodevelopmental disorder and can be early detected with the aid of screening tools. Chandigarh autism screening instrument (CASI) is a newly developed tool to screen autistic symptoms among children aged between 1.5 to 10 years in the north Indian Hindi speaking population. Objective In this study, we evaluated the caregiver report of autistic symptoms in preschool children (3–6 years) attending selected schools of Rohtak. Methods The index study was conducted among 225 caregivers of school-going children aged between 3 to 6 years. Social and communication disorders checklist (SCDC-Hindi) and CASI was used to measure autistic symptoms. The modified Kuppuswamy scale was used for assessing the socioeconomic status of the caregivers. Results The autistic symptoms varied from 2.2 to 18.7%, depending upon the CASI (cutoff score of 10) and SCDC (cutoff score of 9) measurements. The items in the shorter four-item version (CASI Bref) of CASI were found to be the predictors of autistic symptoms in this population. Children’s gender, age, and socioeconomic status were not found to have any association with autistic symptoms in this setting. Conclusion The study provides preliminary evidence in relation to the CASI-linked screening for autistic symptoms among preschool children. The shorter version of CASI (CASI Bref) can be an efficient quick screener for autistic traits, but the full version of CASI needs to be validated as per age-appropriate autism screening tools.

scholarly journals The Child Behavior Checklist as a Screening Instrument for PTSD in Refugee Children

Children ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 521
Author(s):  
Ina Nehring ◽  
Heribert Sattel ◽  
Maesa Al-Hallak ◽  
Martin Sack ◽  
Peter Henningsen ◽  
...  
Keyword(s):  
Child Behavior ◽  
Child Behavior Checklist ◽  
High Sensitivity ◽  
Roc Curves ◽  
Structured Interview ◽  
Screening Instrument ◽  
Screening Tools ◽  
Refugee Children ◽  
Post Traumatic Stress ◽  
Sensitivity Specificity

Thousands of refugees who have entered Europe experienced threatening conditions, potentially leading to post traumatic stress disorder (PTSD), which has to be detected and treated early to avoid chronic manifestation, especially in children. We aimed to evaluate and test suitable screening tools to detect PTSD in children. Syrian refugee children aged 4–14 years were examined using the PTSD-semi-structured interview, the Kinder-DIPS, and the Child Behavior Checklist (CBCL). The latter was evaluated as a potential screening tool for PTSD using (i) the CBCL-PTSD subscale and (ii) an alternative subscale consisting of a psychometrically guided selection of items with an appropriate correlation to PTSD and a sufficient prevalence (presence in more than 20% of the cases with PTSD). For both tools we calculated sensitivity, specificity, and a receiver operating characteristic (ROC) curve. Depending on the sum score of the items, the 20-item CBCL-PTSD subscale as used in previous studies yielded a maximal sensitivity of 85% and specificity of 76%. The psychometrically guided item selection resulted in a sensitivity of 85% and a specificity of 83%. The areas under the ROC curves were the same for both tools (0.9). Both subscales may be suitable as screening instrument for PTSD in refugee children, as they reveal a high sensitivity and specificity.

scholarly journals Association between socioeconomic status and dental caries among Chinese preschool children: a cross-sectional national study

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e042908
Author(s):  
Tingting Zhang ◽  
Jialan Hong ◽  
Xueting Yu ◽  
Qiulin Liu ◽  
Andi Li ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Preschool Children ◽  
Oral Health ◽  
Dental Caries ◽  
Household Income ◽  
Parental Education ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Index Of Inequality

ObjectivesSocioeconomic inequalities in oral health are often neglected in oral health promotion. This cross-sectional study assessed the association between dental caries and socioeconomic status (SES) among preschool children in China.DesignCross-sectional study.SettingData from the Fourth National Oral Health Survey of China (2015), comprising of 40 360 children aged 3–5 years was used.MethodsDental caries indicators including prevalence of dental caries, dental pain experience and number of decayed, missing and filling teeth (dmft). SES indicators included parental education and household income. The associations between SES and dental caries were analysed by using negative binomial regression or Poisson regression models according to data distribution. Relative and absolute inequalities in dental caries were quantified by using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII), respectively.ResultsThere were significant associations between SES and prevalence of dental caries and dmft (p<0.001). Children from lower educated (RII 1.36, 95% CI 1.3 to 1.43; SII 0.97, 95% CI 0.81 to 1.13) and lower household income (RII 1.17, 95% CI 1.11 to 1.24; SII 0.55, 95% CI 0.35 to 0.75) families had higher dmft than those from well-educated and most affluent families. Relative and absolute inequalities in dental caries were larger in urban areas by household income, and in rural areas by parental education.ConclusionsAssociation between dental caries and SES was demonstrated and socioeconomic inequalities in dental caries existed among Chinese preschool children.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Neurodevelopmental Trajectories and Clinical Profiles in a Sample of Children and Adolescents With Early- and Very-Early-Onset Schizophrenia

2021 ◽  
Vol 12 ◽  
Author(s):  
Maria Pontillo ◽  
Roberto Averna ◽  
Maria Cristina Tata ◽  
Fabrizia Chieppa ◽  
Maria Laura Pucciarini ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Neurodevelopmental Disorders ◽  
Early Onset ◽  
Social Role ◽  
Treatment Plan ◽  
Neurodevelopmental Disorder ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Early Onset Schizophrenia ◽  
Assessment And Treatment

Schizophrenia before the age of 18 years is usually divided into two categories. Early-onset schizophrenia (EOS) presents between the ages of 13 and 17 years, whereas very-early-onset schizophrenia (VEOS) presents at or before the age of 12 years. Previous studies have found that neurodevelopmental difficulties in social, motor, and linguistic domains are commonly observed in VEOS/EOS patients. Recent research has also shown a high prevalence of neurodevelopmental disorders (e.g., intellectual disability, communication disorders, autism spectrum disorder, neurodevelopmental motor disorders) in VEOS/EOS patients, indicating genetic overlap between these conditions. These findings lend support to the neurodevelopmental continuum model, which holds that childhood neurodevelopmental disorders and difficulties and psychiatric disorders (e.g., schizophrenia) fall on an etiological and neurodevelopmental continuum, and should not be considered discrete entities. Based on this literature, in this study we focused on the overlap between neurodevelopmental disorders and schizophrenia investigating, in a large sample (N = 230) of VEOS/EOS children and adolescents, the clinical differences, at the onset of psychosis, between VEOS/EOS with neurodevelopmental disorder or neurodevelopmental difficulties and VEOS/EOS with no diagnosed neurodevelopmental disorder or neurodevelopmental difficulties. The findings showed that, in children and adolescents with a neurodevelopmental disorder or neurodevelopmental difficulties, psychosis onset occurred at an earlier age, was associated with more severe functional impairment (e.g., global, social, role), and was characterized by positive symptoms (e.g., grandiose ideas, perceptual abnormalities, disorganized communication) and disorganized symptoms (e.g., odd behavior or appearance, bizarre thinking). Instead, in children and adolescents without a neurodevelopmental disorder or neurodevelopmental difficulties, psychosis onset was mainly characterized by negative symptomatology (e.g., social anhedonia, avolition, expression of emotion, experience of emotions and self, ideational richness). Given these differences, the presence of a neurodevelopmental disorder or neurodevelopmental difficulties should be carefully investigated and integrated early into the assessment and treatment plan for VEOS/EOS patients.

Screening for Borderline Personality Disorder With the McLean Screening Instrument: A Review and Critique of the Literature

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Mark Zimmerman ◽  
Caroline Balling
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
High Sensitivity ◽  
Screening Instrument ◽  
Borderline Personality ◽  
Self Report ◽  
Cutoff Score ◽  
Cutoff Scores ◽  
Diagnostic Detection

Borderline personality disorder (BPD) is underdiagnosed in clinical practice. One approach towards improving diagnostic detection is the use of screening questionnaires. It is important for a screening test to have high sensitivity because the more time-intensive/expensive follow-up diagnostic inquiry will presumably only occur in patients who are positive on the initial screen. The most commonly studied self-report scale specific for BPD is the McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD). We summarize the performance of the scale across studies, examine the performance of the scale using different cutoff scores, and highlight the approach used by investigators in recommending a cutoff score. Most studies of the scale have taken a case-finding approach in deriving the cutoff score on the scale instead of a screening approach. For the purposes of screening, it may be more appropriate for the cutoff score on the MSI-BPD to be less than the currently recommended cutoff of 7.

Validation of the Sinhala Version of the Addenbrooke’s Cognitive Examination-Revised for the Detection of Dementia in Sri Lanka: Comparison with the Mini-Mental Status Examination and the Montreal Cognitive Assessment

2019 ◽  
Vol 47 (4-6) ◽  
pp. 198-208 ◽  
Author(s):  
Vindika Suriyakumara ◽  
Srinivasan  Srikanth ◽  
Ruwani  Wijeyekoon ◽  
Harsha  Gunasekara ◽  
Chanaka  Muthukuda ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Diagnostic Accuracy ◽  
Cognitive Assessment ◽  
Intraclass Correlation ◽  
Screening Tools ◽  
Montreal Cognitive Assessment ◽  
Cutoff Score ◽  
Cognitive Screening ◽  
Dementia Prevalence ◽  
Addenbrooke’S Cognitive Examination

Background: Sri Lanka is a rapidly aging country, where dementia prevalence will increase significantly in the future. Thus, inexpensive and sensitive cognitive screening tools are crucial. Objectives: To assess the reliability, validity, and diagnostic accuracy of the Sinhalese version of the Addenbrooke’s Cognitive Examination-Revised (ACE-R s). Method: The ACE-R was translated into Sinhala with cultural and linguistic adaptations and administered, together with the Sinhala version of the Montreal Cognitive Assessment (MoCA), to 99 patients with dementia and 93 gender-matched controls. Results: The ACE-R s cutoff score for dementia was 80 (sensitivity 91.9%, specificity 76.3%). The areas under the curve for the ACE-R s, Mini-Mental State Examination (MMSE) and MoCA were 0.90, 0.86, and 0.86, respectively. The ­ACE-R s had good interrater reliability (intraclass correlation = 0.94), test-retest reliability (intraclass correlation = 0.99), and internal consistency (Cronbach’s α = 0.8442). Conclusions: The ACE-R s is sensitive, specific and reliable to detect dementia in persons aged ≥50 years in a Sinhala-speaking population and its diagnostic accuracy is superior to previously validated tools (MMSE and MoCA).

scholarly journals Use of the LENA Autism Screen with Children who are Deaf or Hard of Hearing

Medicina ◽  
2019 ◽  
Vol 55 (8) ◽  
pp. 495
Author(s):  
Mark VanDam ◽  
Christine Yoshinaga-Itano
Keyword(s):  
Hard Of Hearing ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Screening Tools ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Screening Process ◽  
Environment Analysis ◽  
Additional Tool ◽  
Review Reports

Background and Objectives: This systematic review reports the evidence from the literature concerning the potential for using an automated vocal analysis, the Language ENvironment Analysis (LENA, LENA Research Foundation, Boulder, CO, USA) in the screening process for children at risk for autism spectrum disorder (ASD) and deaf or hard of hearing (D/HH). ASD and D/HH have increased comorbidity, but current behavioral diagnostic and screening tools have limitations. The LENA Language Autism Screen (LLAS) may offer an additional tool to disambiguate ASD from D/HH in young children. Materials and Methods: We examine empirical reports that use automatic vocal analysis methods to differentiate disordered from typically developing children. Results: Consensus across the sampled scientific literature shows support for use of automatic methods for screening and disambiguation of children with ASD and D/HH. There is some evidence of vocal differentiation between ASD, D/HH, and typically-developing children warranting use of the LLAS, but additional empirical evidence is needed to better understand the strengths and weaknesses of the tool. Conclusions: The findings reported here warrant further, more substantive, methodologically-sound research that is fully powered to show a reliable difference. Findings may be useful for both clinicians and researchers in better identification and understanding of communication disorders.

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