Attention Deficit Hyperactivity Disorder (ADHD) and Associated Factors Among First-Year Elementary School Students

Prevalence of ADHD and factors for parent's participation in the care of children with ADHD, Yasothon, Thailand

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is a mental health disorder commonly observed in children. Parents' participation in the care (PPC) of children with ADHD (C-ADHD) is especially important to promote support care and treatment. We estimated the prevalence of C-ADHD and identified the factors for the PPC of C-ADHD.

METHODS AND MATERIALS

A cross-sectional analytic study was conducted on parents and grade-one students in five primary schools, Muang district, Yasothon, Thailand. The study was conducted from March to June 2022. Data were collected using the screening test of SNAP-IV Thai version and by interviewing child psychiatrists and parents. Descriptive statistics were used to summarize the demographic data and prevalence of C-ADHD. Unconditional multiple logistic regression was used to identify predictive statistical model for the PPC of C-ADHD.

RESULTS

Four hundred sixty-four students were screened by SNAP-IV, of which 30 students were confirmed as ADHD (6.5% [95% confidence interval [CI]: 4.4-9.1]), with 9.1% boys and 3.6% girls. Four factors associated with the PPC of C-ADHD significantly including male (ORadj: 2.5, 95% CI: 1.4-4.4), single divorce status (ORadj: 2.1, 95% CI: 1.1-4.2), income more than 15,000 baht (ORadj: 3.1, 95% CI: 1.8-5.1), and attitude level during middle-high (ORadj: 8.4, 95% CI: 5.0-14.2). Predictive factors of four variables can be used to implement the policy to improve the healthcare system, prevention, and PPC of C-ADHD.

Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6-12 years) have been diagnosed with ADHD (2012-2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD.

Predicting Children with ADHD Using Behavioral Activity: A Machine Learning Analysis

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (<0.05). Eight ML-based classifiers such as random forest (RF), Naïve Bayes (NB), decision tree (DT), XGBoost, k-nearest neighborhood (KNN), multilayer perceptron (MLP), support vector machine (SVM), and 1-dimensional convolution neural network (1D CNN) were adopted for the prediction of children with ADHD. The average age of the children with ADHD was 12.4 ± 3.4 years. Our findings showed that RF-based classifier provided the highest classification accuracy of 85.5%, sensitivity of 84.4%, specificity of 86.4%, and an AUC of 0.94. This study illustrated that LR with RF-based system could provide excellent accuracy for classifying and predicting children with ADHD. This system will be helpful for early detection and diagnosis of ADHD.

Review of rodent models of attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a polygenic neurodevelopmental disorder that affects 8-12 % of children and >4 % of adults. Environmental factors are believed to interact with genetic predispositions to increase susceptibility to ADHD. No existing rodent model captures all aspects of ADHD, but several show promise. The main genetic models are the spontaneous hypertensive rat, dopamine transporter knock-out (KO) mice, dopamine receptor subtype KO mice, Snap-25 KO mice, guanylyl cyclase-c KO mice, and latrophilin-3 KO mice and rats. Environmental factors thought to contribute to ADHD include ethanol, nicotine, PCBs, lead (Pb), ionizing irradiation, 6-hydroxydopamine, neonatal hypoxia, some pesticides, and organic pollutants. Model validation criteria are outlined, and current genetic models evaluated against these criteria. Future research should explore induced multiple gene KOs given that ADHD is polygenic and epigenetic contributions. Furthermore, genetic models should be combined with environmental agents to test for interactions.