Aims: This case series aimed to evaluate the effects of treatment for allergic rhinitis (AR) in AR-diagnosed children with previous diagnosis of tic disorders/attention-deficit/hyperactivity disorders (TD/ADHD) but unresponsive to behavioral or medical treatment. Materials and methods: Between July 2016 and June 2021, children diagnosed with AR in our hospital were enrolled. All were diagnosed with TD/ADHD refractory to behavioral or medical treatment. The demography and clinical information were collected from medical records. The outcomes were visual analogue scale (VAS) for AR severity, Yale Comprehensive Tic Severity Scale (YGTSS) for TD symptoms, and Attention-Deficit Hyperactivity Screening Scale (SNAP-IV) for ADHD symptoms. Results: A total of 27 children  were included, with a mean age 7.4 ± 2.9 years (3 – 17 years). They had undergone behavioral or medical treatment of TD/ADHD for 3.6 ± 1.9 years but without significant improvement in TD/ADHD symptoms. After 2-6 months of systematic treatment for AR, VAS was decreased to 0.4 ± 0.1 from 0.8 ± 0.2, YGTSS to 3.5 ± 0.7 from 6.8 ± 1.4, and SNAP-IV to 0.4 ± 0.1 from 0.6 ± 0.2 . No recurrence of TD/ADHD symptoms was reported during a mean follow-up of 2.4 ± 1.1 years (0.5 – 5 years). Conclusion: AR treatment improves TD/ADHD outcomes in children with difficult-to-treat TD/ADHD. In TD/ADHD children who are unresponsive to behavioral or drug treatment and have AR-related symptoms, AR examination and treatment are recommended for better prognosis.  Allergic rhinitis (AR) is a common allergic disease in children. AR seriously affects their quality of life, learning, leisure time, and school attendance. Many studies have shown associations between allergic diseases and tic disorder/attention-deficient/hyperactivity disorders (TD/ADHD). Children with TD or ADHD have a high prevalence of allergic diseases including AR, asthma, atopic dermatitis, and allergic conjunctivitis , 10, 11.TD is characterized by locally occurring, involuntary, frequent, stereotyped muscle twitch. Motor tics usually occur with rapid muscle contraction movements of the fingers, face, neck, shoulders, trunk, and extremities; vocal tics usually occur with contractions of the mouth, nose, throat, and respiratory muscles, and vocalization via airflow through the nose, mouth, and throat. The prevalence of TD in China is ~ 4 – 23% . It is mPrevious studies reported that the ADHD scores of drug-naïve ADHD children with AR symptoms were improved after AR treatment , 11. Thith Edition (DSM-5) or Chinese Mental Disorders and Diagnostic Criteria 3rd Edition (CCMD-3) [Between July 2016 and June 2021, children diagnosed with AR with a previous diagnosis of TD/ADHD were enrolled in this case series. Inclusion criteria: 1) previous diagnosis of TD/ADHD in the psychiatry specialist hospital or children’s hospital. The diagnostic criteria for TD/ADHD by pediatric psychiatrists were clinically assessed in accordance with the American Mental Illnesses Diagnostic and Statistical Manual 5(CCMD-3) , 16, 17.(CCMD-3) , 18. TheThe level of TD/ADHD symptoms was evaluated with relevant quantitative data included total Yale Comprehensive Tic Severity Scale (YGTSS) score and Attention-Deficit Hyperactivity Screening Scale (SNAP-IV) scores. The YGTSS score used for the assessment of TD was based on a total score of 100, with 1 – 25 rated as mild, 25 – 50 as moderate, and > 50 as severe . The SNAThe evaluation of AR severity was based on the visual analogue scale (VAS) score data of nasal symptoms before and after treatment. VAS < 5 cm (2.4 – 5.0) is mild rhinitis, > 6 cm (5.3 – 7.7) is moderate to severe rhinitis . For basic characteristics of subjects, continuous variables are presented as means with standard deviations (SDs); categorical variables are presented as counts and percentages. Paired t-test was used to compare the AR symptom scores and TD/ADHD scores. Two-tailed p < 0.05 indicates statistical significance. The statistical analyses were performed with SAS software version 9.2 . A total of 27 children (18 boys and 9 girls) were included. The demographic and clinical characteristics at admission to the otolaryngology outpatient department are listed in All patients were previously diagnosed with TD/ADHD in a psychiatric clinic, including 19 with TD, 6 with TD/ADHD, and 2 with ADHD. All had undergone behavioral or medical therapy for TD/ADHD for 3.6 ± 1.9 years (0.5 – 9 years) but without further improvement. After AR treatment, YGTSS  and SNAP-IV  scores were significantly improved . 9/L). Serum total IgE 79 IU/mL , sIgE showed house dust mite grade 4, dust mite grade 5, German cockroach grade 1, mugwort grade 3, ragweed grade 1, shrimp grade 2, crab grade 1. Preliminary diagnosis was seasonal aggravation of perennial AR. The patient was a 13-year-old boy who had frequently rubbed his nose, blinked, opened his mouth, and coughed for 8 years. Seven years ago, he was diagnosed with chronic TD at the local pediatric psychiatry department, but the psycho-behavioral therapy did not achieve satisfactory effects. His TD symptoms worsened, manifested as motor tics and vocal tics, with normal electroencephalogram and cranial magnetic resonance, normal intelligence tests, and a YGTSS score of 67. He was diagnosed with Tourette syndrome. Two tablets of Changma Xifeng 3 times a day were administered, but no effect was obvious after 1 year of treatment. The medication was changed to tiapride hydrochloride tablets 50 mg, 3 times a day, but there was still no improvement of symptoms after half a year. He visited the otolaryngology outpatient clinic 5 years ago. Supplementary medical history after inquiries showed that he had recurrent eczema as a baby which was significantly improved after the age of 1, but urticaria often occurred. His father was diagnosed with TD in childhood, and was allergic to dust, cold air, and crabs. His younger sister had eczema, frequent nose itching, sneezing, and nasal congestion. The patient’s current symptoms were nasal itching, sneezing, runny nose, and nasal congestion. The nasal mucosa was pale and edematous, the inferior turbinate was hypertrophied, and the conjunctiva was slightly congested. Skin prick test showed house dust mite +++, dust mite ++++ , elm +, birch +, mugwort +++, ragweed +, shrimp ++, crab ++. Peripheral blood eosinophils were 8.3%  Control of home environment, reduce dust mites, monitor and remove mites at home once every 2 – 4 weeks; wear masks and anti-pollen glasses when going out in spring and autumn; 2) mometasone furoate nasal spray, once daily; montelukast sodium chewable tablets 5 mg and cetirizine hydrochloride drops 5 mg, orally, once a night; 3) after allergy season or after allergy symptoms were controlled, sensitivity treatment was stopped; 4) nasal wash with normal saline 2 – 3 times/day; 5) tiapride hydrochloride, 50 mg, 3 times a day. After using those treatments for 1 month, the patient’s AR symptoms were significantly improved by 70%. Then, the dose of tiapride hydrochloride tablets was reduced from 50 mg twice a day for a week to 50 mg once a day for a week, and then discontinued. After treatment for 3 months, neither AR symptoms nor blinking, mouth opening, throat clearing, or other tics were observed. After treatment for 6 months, antiallergic drugs were only used when the symptoms were aggravated by cold air, and only sublingual desensitization treatment with dust mite drops was used. Sublingual desensitization therapy has been performed for 5 years, and both AR and TD/ADHD symptoms are well controlled. Alternaria ++, juniper +++, willow ++, sycamore, ashwagandha +, Artemisia maxima ++++, Humulus japonicus +++, egg white, peanuts, milk +. Serum total IgE 760 KAU/L, sIgE house dust mite grade 5, dust mites grade 6, mold combination grade 6, mugwort grade 5, ragweed grade 4, eggs, peanuts, and milk grade 1. FeNO 39 ppb, pulmonary function test results showed mild obstructive ventilatory dysfunction, FEV1 improvement rate was 13.6%, bronchodilation test was positive. Video nasolaryngoscopy revealed hypertrophic adenoids occupying 4/5 of the posterior nostrils, and the airway was significantly narrowed. Pure tone audiometry showed bilateral conduction deafness, air conduction-bone conduction examination was 20 dBHL, and acoustic immittance test binaural B-type chart. Preliminary diagnosis: allergic rhinitis, allergic asthma, adenoid hypertrophy, tonsil hypertrophy, childhood snoring, bilateral secretory otitis media. This 11-year-old girl had limb hyperactivity, winking, nose rubbing, puffed cheeks, inattention, and poor academic performance for 9 years. The child has winked and rubbed her nose frequently since she was 2 years old. It was obvious in spring and autumn, and it gradually became more and more perennial. At 6 years old, she repeatedly puffed out her cheeks. At 7 years old, she often coughed. At 8 – 9 years old, she repeatedly shook her head and stretched her limbs. When she was 4 years old, she was diagnosed with Tourette syndrome combined with mixed ADHD. The EEG, dynamic EEG, cranial magnetic resonance, and intelligence test were normal. The YGTSS was 73 points, SNAP-IV was 68 points. Psycho-behavioral therapy has been carried out until the age of 6, but no effect was obvious. After the age of 6, she used clonidine transdermal patch 1 mg/once a week and Ning-dong granules 6 g orally twice a day for half a year. Due to the lack of obvious effect, haloperidol was used from the initial dose of 0.5 mg to 1 mg orally twice a day for 1 year, but the symptoms only improved by 30 – 40%. At the age of 8, she changed to aripiprazole tablets 10 mg/day for 1 year. The frequency of limb hyperactivity decreased to about once every 10 – 20 minutes, but the symptom improvement remained at 30 – 40%. The patient’s academic performance declined year by year, and she came to our hospital for further diagnosis and treatment. She had a past history of eczema, urticaria, and contact dermatitis. Both mother and younger brother had a history of AR. Supplementary medical history after questioning found that the child suffered from shortness of breath, wheezing, frequent coughing and grunting, eyelid and facial twitching, shrugging, long-term night-time snoring, mouth breathing, often waking up, often sleeping on the stomach, often ignoring questions from parents and teachers, and a significant drop in academic performance. Physical examination showed red and swollen nasal mucosa, hypertrophy of inferior turbinate, obvious shadow of the lower eyelids, and large grade 3 tonsils. Skin prick test showed house dust mites and dust mites ++++, Dermatophagoides farinae drops; 6) nasal wash with normal saline 2 – 3 times a day; 7) aripiprazole tablets 10 mg/day were continued, and the dose was gradually reduced after the condition was stable. After 4 omalizumab administrations, the tic symptoms and hyperactivity disappeared; after 14 omalizumab administrations, all symptoms were in good control, and her academic performance was significantly improved. The treatments were: 1) Control of home environment to reduce dust mites and molds, measure and remove mites at home once every 2 – 4 weeks, wear masks and anti-pollen glasses when going out in spring and autumn; 2) mometasone furoate nasal spray, oral montelukast sodium chewable tablets, cetirizine hydrochloride drops, eucalyptus lime and pinene soft capsules; budesonide formoterol powder inhalation 4.5 μg, 2 times a day. 3) after the lung function was significantly improved, subcutaneous injection of omalizumab 300 mg was administrated once a month; 4) adenoidectomy, tonsillectomy, and bilateral middle ear cannulation under general anesthesia; 5) sublingual desensitization treatment with Studies have shown that ADHD scores are significantly improved in drug-naïve ADHD children with AR symptoms after treatment of AR . The preIn the present study, skin prick test showed that mite is the main allergen (92%), and desensitization therapy efficiently improved tic symptoms and hyperactivity in 81.5% of patients after cleaning environment. Yang et al.  reportedThe study has several limitations. First, it has an inherent limitation due to its retrospective nature. Second, the sample size was small. Third, there was no placebo group. Although the reasons why the included TD/ADHD children were unresponsive to behavioral or medical treatment remains further investigation, their TD/ADHD scores were significantly improved after systemic AR treatment. AR treatment improves clinical outcomes of difficult-to-treated TD/ADHD in children with AR symptoms. In TD/ADHD children who are unresponsive to behavioral or drug treatment for TD/ADHD and who have AR-related symptoms, a family history of allergic diseases, or an aggravation of seasonal symptoms, AR examination and treatment are recommended for better prognosis. The study was reviewed and approved by the ethics committee of Beijing Shijitan Hospital, Capital Medical University. All parents gave their written informed consent. Rui-Li Yu: literature research; clinical studies; experimental studies; data acquisition; data analysis; statistical analysis; manuscript preparation. Jing Wang: definition of intellectual content; clinical studies; experimental studies; statistical analysis; manuscript editing. Xue-Song Wang: literature research; experimental studies; data acquisition; data analysis; statistical analysis; manuscript editing. Hong-Tian Wang: guarantor of integrity of the entire study; study concepts; study design; definition of intellectual content; manuscript review. Xue-Yan Wang: guarantor of integrity of the entire study; manuscript editing; manuscript review . All authors read and approved the final manuscript. This study was supported by the Project of Beijing Key Laboratory for clinical rational drug use biometric spectroscopy evaluation (2020-KF08); Joint key project of Beijing Municipal Education Commission and Beijing Municipal Natural Science Foundation (KZ202110025030). All authors declare to have no conflict of interest.


Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization. Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders, affecting ~5–8% of children worldwide , 2. For Extensive studies have attempted to characterize ADHD in terms of neuropsychological performance, brain anatomy and functional responses, and genetic risk factors. Cognitive deficits in executive function, reaction time, vigilance, inhibition control, sustained attention, and working memory have been reported in ADHD –18. NeurThe increasing accessibility of machine learning models has led to increased interest in applying such models to investigate psychiatric disorders. Generally, machine learning models are mathematical models that learn complex patterns in an existing dataset. These learned patterns can then be used for prediction in a novel dataset , as well as to highlight the most important variables in creating this prediction. Machine learning models have proved effective in capturing the complex interactions between discrete alterations in schizophrenia, Alzheimer’s disease, and autism spectrum disorder (ASD) , 29. MosIncreasing evidence suggests that machine learning techniques are beneficial in improving ADHD diagnosis, understanding neurobiological substrates, and evaluating treatment strategies (for reviews –35). ForThe majority of existing machine learning studies in ADHD focus on developing classification algorithms between ADHD patients and controls or patients with other comorbid disorders. Undoubtedly, machine learning algorithms were best suited for predictive purposes. However, the sample size is often a study limitation. Large and high-quality datasets are difficult to obtain due to the substantial efforts required in the collection and maintenance processes. Studies in small-size samples tend to get over positive results and less generalizable models without including appropriate validation processes  , 39. ForExisting reviews have summarized the effectiveness of different machine learning models in differentiating subjects with ADHD from control subjects or subjects with other disorders –35. HoweThe use of machine learning in aiding the diagnosis of ADHD has been covered extensively in existing reviews , 34, 35 A recent nationwide study in Sweden applied multiple machine learning models, including random forest, elastic net, deep neural network, and gradient boosting, in identifying the significant predictors for ADHD based on family and medical histories from 238,696 individuals . The besBeyond allowing the classification of subjects into traditional diagnostic groups, research into machine learning-aided diagnosis of ADHD has contributed to the understanding of the clinical presentation and heterogeneity of ADHD by allowing the identification of novel subgroupings of participants, which can increase diagnostic accuracies , 60. ForIn general, the accuracy of the classification models varies from 66 to 96% in the existing machine learning studies that investigated behavioral and cognitive performances in ADHD. The inconsistency was partially contributed by the differences in total sample size, percentage of the clinical group in the total sample, test or measurement selection, model selection, or validation methods. Therefore, extra precaution was required in designing reliable classification models. Furthermore, machine learning techniques that can explore the heterogeneities in ADHD  may not only improve diagnosis but may contribute to improvements in future research investigating the underlying mechanisms by providing more appropriately defined samples.The neuroanatomy of ADHD has been investigated for decades. However, results are inconsistent , 64, 65.Machine learning, on the other hand, can model a number of features simultaneously, making machine learning approaches particularly well-suited to understanding the widespread structural alterations underpinning ADHD. For example, Peng et al. reported results from an extreme learning machine-based classification model which differentiated children with ADHD and controls with an accuracy of 90.18% using sMRI data from ADHD-200 . The modMachine learning can also be used to explore novel sMRI features, which may provide optimal discriminative power for ongoing research into ADHD. For example, Chang et al. generated novel morphological features based on the local binary patterns (an image texture categorization method) to differentiate data from 210 ADHD and 226 controls from the ADHD-200 dataset . An SVM Task-based fMRI is a commonly used method to examine brain activation or functional connectivity during of engagement of a specific cognitive domain. Features like voxel-level activation, functional connectivity between regions-of-interest (ROIs), or network topological properties , which may otherwise be undetected when using traditional methods. For example, Wolfers et al. applied a Gaussian process classifier in differentiating subjects with ADHD, their unaffected siblings, and controls based on the fMRI data during stop-signal task . The modThe brain demonstrates intrinsic spontaneous activity that can be measured during rest. rs-fMRI measures such activity, and the collected data can be used to generate machine learning features, such as regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuation (fALFF), and network connectivity. As rs-fMRI data does not require the performance of a task, it is easy to implement in children with ADHD. Classification techniques have highlighted regions in which resting brain activity is of potential importance in ADHD. For example, studies using SVM have revealed that functional connectivity in default mode network, frontoparietal regions, cerebellum, precuneus/posterior cingulate cortex regions, and dorsal anterior cingulate cortex were important in differentiating ADHD , 84.As previously mentioned, the ADHD-200 dataset has allowed numerous investigations into rs-fMRI correlates of ADHD using machine learning algorithms. Various rs-fMRI features have been explored, including ReHo, fALFF, power spectra, functional connectivity, and voxel- and ROI-level functional networks –87. EloyDespite the success of rs-fMRI-based machine learning models, it is possible that phenotypic information such as gender, age, and cognitive measures provide more discriminative power than rs-fMRI data , 93. HowDue to its high accessibility, low cost, and non-invasive nature, EEG has gained popularity in studying ADHD. Common features generated from EEG data are power in frequency bands at different locations and event-related potentials (ERPs), which are electrical responses that are time-locked to the occurrence of sensory or cognitive processes, as shown in Fig. Several studies have investigated the predictive power of specific features of EEG data. For example, using deep neural network, one study identified that ERPs within the time range from 100 to 200 ms post-stimulus are important in differentiating children with ADHD and controls during an interval-time task . The modMachine learning is also valuable in investigating novel EEG features. For example, Kim et al. used machine learning to validate mismatch negativity  in differentiating adults with ADHD from controls . The SVMFunctional near-infrared spectroscopy (fNIRS) is a non-invasive and portable method to measure the hemodynamic response in the cortex. Relative to fMRI, fNIRS is less susceptible to the movements and is therefore well-suited to study ADHD, and machine learning has the potential to utilize the fNIRS’s high temporal resolution while overcoming its low spatial resolution. One study applied SVM on fNIRS data from children with ADHD and controls during a working memory task . The finGiven its ability to model several features simultaneously, machine learning is well-suited to multimodal investigations of neural markers of ADHD. For example, Zhou et al. combined rs-fMRI with sMRI and DTI data from the ABCD dataset and reported that the functional connectivity in frontal and temporal regions, cerebellum, thalamus, and anatomical regions in the basal ganglia were the most discriminative features for ADHD in children . Luo et Genetic and twin studies suggest that ADHD is highly heritable –119. Thivan der Meer et al. used a random forest regression model to investigate the predictive power of 29 stress-related genes on ADHD severity in children with ADHD, subthreshold ADHD, and controls . The modMachine learning algorithms allow the combination of genetic data with data such as cognitive and neuroimaging data. For example, using conditional random forests, Sudre et al. were able to predict ADHD severity with an AUC of 0.79 . While cHeterogeneity in ADHD imposes difficulties in developing effective and reliable treatment strategies. Methylphenidate (MPH) is one of the main pharmacological treatments for ADHD; however, 30% of patients are poor responders , 27. MacMachine learning can also be utilized to predict adverse drug outcomes, which are common in ADHD treatment. For example, Yoo et al. predicted sleep side effects of using MPH treatment based on multiple variables and achieved an accuracy of 95.5% . Based oA growing number of studies are utilizing machine learning techniques to report interpretable results regarding neural mechanisms associated with ADHD, in addition to building accurate classification models. Such studies have already contributed to the literature regarding functional, structural, and physiological correlates of ADHD.A particular benefit of classification models is the ability to label individuals. In addition to the detection of important features, machine learning can assist the development of individualized treatment plans for ADHD e.g., , 135). T. T135]).The existing classification models for ADHD reported largely inconsistent accuracy, with the majority varying from 60 to 90%. Several factors related to machine learning design may contribute to these discrepancies. First, the choice of machine learning algorithms may affect the performance of the classification model based on different datasets. Algorithms with very few or no trainable parameters were preferable for studies with small sample sizes, whereas studies with large datasets were able to explore the effectiveness of deep learning algorithms , 73, 133A major benefit of machine learning techniques is that they always involve multivariate data, and some machine learning models like SVM and random forest can rank the contribution of features under the interaction of each other . TherefoHowever, several factors limit the interpretability of the important features reported in existing machine learning studies. First, not all of the machine learning models have intrinsic operations to rank the input features for their importance during the learning procedures. Although generalized feature importance methods exist, such as permutation importance, these methods do not necessarily represent the covariate information used in the original models , 142. OnDespite great promise, challenges are also present before machine learning can provide significant clinical benefits for ADHD due to its heterogeneity. First, machine learning algorithms currently lack interpretability. High-accuracy models are usually constructed with a collection of variables , 122, wiMachine learning techniques are still currently undergoing extensive development. Several directions have the potential to resolve the existing problems. One direction is taking a generative approach. Most existing machine learning studies have utilized discriminative models focused on finding the boundaries between known groups within a sample. On the contrary, generative models focus on characterizing groups and predicting group allocation based on probability, as shown in Fig. Another direction is taking a dimensional approach. The categorical definition of ADHD may not be sufficient to describe the ADHD symptoms in a comprehensive way. Fair et al. reported distinct ADHD subgroups based on cognitive performance, suggesting that neurobiological properties of ADHD might need to be characterized using multiple cognitive measures in addition to the DSM-based symptom measures . Using rIn summary, early attempts to investigate ADHD using machine learning show promising results. In addition to seeking high classification accuracy, studies using machine learning to study ADHD can identify the importance of features and discriminative power of modalities, which provide clinical and research targets. Future studies focusing on increasing the interpretability and generalizability of models are highly desired.Supplemental material


Childhood mortality and morbidity has become a major public health issue in low-middle-income countries. However, evidence suggested that Low birth weight(LBW) is one of the most important risk factors for childhood deaths and disability.This study is designed to estimate the prevalence of low birth weight (LBW) in India and to identify maternal correlates associated with LBW.Data has been taken from National Family Health Survey 5 (2019–2021) for analysis. 149,279 women belonging to reproductive age group (15–49) year who had last recent most delivery preceding the NFHS-5 survey.Mother's age, female child, birth interval of less than 24 months, their low educational level, low wealth index, rural residence, lack of insurance coverage, women with low BMI, anaemia, and no ANC visits during pregnancy are predictors that contribute to LBW in India. After adjusting for covariates, smoking and alcohol consupmtion is strongly correlated with LBW.Mother’s age, educational attainment and socioeconomic status of living has a highly significant with LBW in India. However, consumption of tobacco and cigarrettes are also associated with LBW.The online version contains supplementary material available at 10.1186/s12884-023-05726-y. The Global public health system continues to face significant obstacles, particularly in low-middle-income countries (LMICs), regarding maternal and child healthcare , 2. One Studies have shown that LBW is the key predictor for neonate and infant mortality They are Advancements in medical technologies have enhanced the survival rates of infants with LBW. However, this have also increased the health care costs of bringing up these children . ChildreA report published by UNICEF-WHO on LBW stated the prevalence as 26.4% in Southern Asia which was five times higher than Eastern Asia 5.1% in 2015. Member States of the World Health Assembly (WHA) 65th session employed the goal of a 30% worldwide decline in low birthweight between 2012 and 2025. Reporting on progress continues to be challenging, though. Since 2000, there has been no significant improvement in the rate of LBW babies, especially from 2010 to 2015. Without accelerated preventative measures, we will not be able to reduce LBW by 30% by 2025. Due to unavailability of data, the regional prevalence for India has not been evaluated in that report .According to WHO (2004), prematurity and LBW account for 18.3 million disability-adjusted life years (DALYs) in the South-East Asian Region . In ordeThere has not been much studies published on the prevalence of low birth weight in India and there is a dearth of national-level source for birth weight statistics, even though India has the greatest burden of LBW data are available either in birth certificate forms or in hospital discharge data forms. The NFHS of India, equivalent to the Demographic Health Survey (DHS), in its third round  collected data on the birth weight of infants by the maternal recall, while asking mothers who had institutional deliveries to show their health cards, where the birth weight of the child is recorded. Similarly, during the NFHS round 5, the birth weight of the child has been recorded.Several changes have taken place since 2005–06, thereby there is a need for a new study explaining on the current scenario of the country regarding LBW. There is also a need for studies investigating potential factors contributing to the high prevalence of LBW in India. There must be a portal for LBW as soon as the child is born, his weight should be recorded and he should be under supervision up to five years of age to prevent under-5 mortality. Findings from such studies can be used to develop interventions and policies focusing LBW in India.1. To estimate the prevalence of Low birth weight for institutional births in India2. To identify the maternal correlates and their association with LBW.3. To determine the association between lifestyle factors such as consumption of alcohol, tobacco, and cigarettes with LBW.This study is based on the data from the National Family Health Survey (NFHS) 5, a nationwide survey to scientifically investigate health and its social determinants and related economics in India. It gives information for 707 disticts, 28 states and 8 union territories. The nationwide data collection spanned from Phase I from 17 June 2019 to 30 January 2020 covering 17 states and 5 UTs and Phase-II from 2 January 2020 to April 2021 covering 11 states and 3 UTs .India is the world's second-most populous country (1.3 billion population), with 28 states and eight union territories (UTs). Each state and UT are further divided into districts. Districts are subdivided into census enumeration blocks and wards in urban areas and villages/taluks in rural areas. For this dissertation, data collected through the women individual schedules were used to create the dependent and independent variables. As the data had been collected as a part of an observational study, the study design for this research is also similar to a cross-sectional study.NFHS 5 gathered information from 636,699 households, 724,115 women, and 101,839 men. Two stages of stratification were used to create the NFHS-5 sample. The sampling frame used to choose the PSUs(Population Sample Units) was the census from 2011. PSUs were Census Enumeration Blocks (CEBs) in urban regions and villages in rural areas. Less than 40-household PSUs were connected to the PSU that was closest to them. Villages were chosen from the sample frame inside each rural stratum with a probability proportionate to size (PPS). Prior to the main survey, a thorough household mapping and listing operation was carried out in each chosen rural and urban PSU. Selected PSUs with at least 300 estimated households were divided into pieces with 100 to 150 households each. The survey used systematic sampling with probability proportional to segment size, and two of the segments were randomly chosen. An NFHS-5 cluster is thus either a PSU or a PSU section. In the second step, 22 homes were randomly chosen using systematic sampling from each chosen rural and urban cluster. Volume II of the national report provides a thorough explanation of sample design, weight computation, standard error estimation, and techniques to improve data quality measurements. In this present study a total of 149,279 women aged 15–59 years were included for the analysis who were interviewed by individual schedule , the det• The most recent child born in the family, to minimize the possibility of change in several maternal correlates over time.• Children delivered in healthcare institutions in India, to eliminate the imprecision of birth weight taken at home.• Single born child, because multiple births such as twins, and triplets (more than one child in one delivery) have an influence on the birth weight of the children.Birth weight data were collected from health cards or hospital discharge cards or mother’s self-reported data.Villages and census enumeration blocks were chosen from districts in rural and urban areas, respectively, through a two-stage sampling procedure. Data collection was done using CAPI  from June 2019 to April 2021 with an inbuilt schedule and proper maintenance of confidentiality of respondents’ answers. NFHS-5 methodology, including selecting households and data collection procedures, has been meticulously described and published .A total number of women participants in the entire study design is 724115 out of which only 176843 (24.42%) participants had most recent birth history preceding the survey. Participants who delivered in healthcare facilities 155624 (21.49%) were considered for further analysis. Women who delivered single child 154129 (21.29%) with evidence of numeric birth weight 149279 (20.61%) was the confirmed study population.Women with previous child birth history 547272 (75.58%), home deliveries 21219 (2.93%), with data of twins 1477(0.95%) and triplets 18 (0.01%) and child not weighted at birth 2714 (1.76%) or don’t know the exact weight 2136 (1.39%) were dropped from the study Fig. Children were classified to have LBW if their birth weight was less than 2500 g.Among the sociodemographic characteristics of mother or family are the mother's age, level of education, wealth index, marital status, religious background, and location of residence. Age of mother, birth order, birth interval, complications during pregnancy, and health behaviours such as smoking and alcohol use were among the reproductive characteristics of the mother. These variables were taken into analysis from the conceptual framework Fig. 2ANC status comprised the initial ANC visit's timing, the total number of ANC visits, the participant's pregnant tetanus shot, the location of the birth, and service accessibility. ANC status has been divided into three sub-categories namely no ANC visits, ≤ 4 ANC visits and > 4 ANC visits .2), “Normal” (18.5– 24.9 kg/m2), “Overweight” (25.0–29.9 kg/m2), and “Obese” (≥ 30.0 kg/m2) [Among the anthropometric measurements are the mother's body mass index was categorized as “Underweight” (less than 18.5 kg/m0 kg/m2) . Anemia 0 kg/m2) .STATA 16.0 was used to clean and analyse the data . For a continuous variable like age, we determined the mean and standard deviation. Additionally, we looked at weighted profiles in both sexes and reported them as percentages and figures . The freThis study is based on secondary data obtained from NFHS 5 (2019–21) with no personal identifiers and hence there is no participant risk. The data were requested from International Institute of Population Sciences (IIPS), Mumbai through proper channel and appropriate permission was taken. The same have been properly acknowledged and referenced wherever required.As a result of literature review, we have identified potential confounders of LBW and its association among different independent variables. We have found that these confounders are also common risk factors of LBW providing were selected and adjusted for it. Variables include place of residence, marital status, birth interval, birth order and alcohol consumption.After applying the inclusion criteria, the number of participants involved were 149,279 within the age range from 15 to 49 years . Correlates such as pregnancy complications, caesarean mode of delivery, employment status, and marital status were not significantly associated with our outcome variable. The findings of the chi-square confirmed disparities between the two groups, which were distinguished by the presence or absence of LBW, in terms of the mother's socioeconomic status, educational attainment and caste group.Table Among the infants with LBW, women of 15 to 24 years had the highest prevalence of LBW 19.09% (95%CI: 18.74–19.44). Women with higher levels of education (13.20%) had a lower prevalence of LBW as compared to women with no primary education (19.37%). Scheduled caste (18.66%) and scheduled tribe (17.81%) women were considered to be vulnerable to LBW infants. Underweight mothers (21.37%) had higher prevalence for LBW as compared to obese (14.48%) and normal BMI (16.70%) of the mother. Women with pregnancy complications had a 16.87% of LBW prevalence. Mothers with no ANC visits (18.90%), no iron supplementations (18.90%), and no tetanus injections during pregnancy (20.10%) had the highest prevalence among any other variables. However, modifiable lifestyle habits such as consuming cigarettes, tobacco (20.29%) and alcohol (18.77%) are other risk factors associated with LBW.Supplementary The univariate regression shows that the age of the mother, gender of the child, place of residence, insurance coverage, BMI status of the mother, wealth index, educational attainment, number of ANC visits, birth order, birth interval, caste, anaemia status, iron supplementation and tetanus injections during pregnancy were significantly associated with LBW Table .Table 4AModifiable lifestyle habits such as the consumption of cigarettes and tobacco and the frequency of alcohol consumption were significantly associated with LBW. Whereas consumption of alcohol  was not significantly associated with LBW. However, mother having history of daily alcohol intake had greater chances of delivering LBW infants as compared to occasional drinkers and non-drinkers.Table p-value < 0.01), whereas LBW was not significant for women with history of alcohol consumption.Table .The aim of the study was to identify potential predictors of low birth weight (LBW) in India, and specifically to investigate how maternal age associated with the likelihood of LBW. The study found that several variables were potentially associated with LBW, including maternal age, the sex of the child, maternal education level, wealth index, religion, insurance coverage, location of residence, maternal body mass index (BMI), anemia status, history of stillbirths, birth spacing, and adequate antenatal care (ANC) visits. The majority of these variables, such as maternal age, education, insurance coverage, BMI, anemia, appropriate birth intervals, and sufficient ANC visits, were identified as modifiable factors that could potentially be improved in order to reduce the risk of LBW.The results of this study support other studies that show female new-borns have a greater risk of LBW than male neonates. Studies confirmed that this might be caused by female foetuses having greater levels of maternal glucose intolerance, which may have an influence on their birth weight , 38.According to studies, singleton pregnancy women (women giving birth for the first time) were more susceptible than multiparous women (women who have given birth previously) with inadequate birth gaps to have a kid who is LBW. The study's results were consistent with this hypothesis. Further research revealed that moms with birth intervals of less than two years were most likely to produce LBW children than mothers with birth intervals of two or more years. These results supported those of earlier investigations, as well –41.Based on the study's findings, mothers with higher educational attainment were less likely to deliver an LBW baby than uneducated mothers. We noticed a dose–response relationship between maternal education level and the likelihood of delivering an LBW infant i.e., the risk of having LBW child decreases with an increase in educational level of mothers. This was in line with the outcomes of other research conducted in India utilizing the NFHS-3 data , 30. In Insurance coverage had a crucial role in LBW in India. The National Health Mission (NHM) in India has been working for more than 15 years with the goals of expanding service coverage, maximising equity in the health sectors, and improving health outcomes, while focusing solely on reducing out-of-pocket expenditure (OOPE) and catastrophic health spending (CHS), particularly among the most disadvantaged, deprived, and vulnerable groups. However, no such insurance policies were available that can uptake the financial hardship for LBW child or sick child , 44. ComRural mothers had a protective factor of having an LBW baby, which was different from NFHS-3 findings. This outcome might be attributed to better dietary habits and novel prenatal care procedures –48.Low micronutrient intake was associated with a low BMI. Foetal development may be hampered by pregnant mothers having low nutritional levels. According to research by Ramana and colleagues, consuming 30% to 50% more protein overall might lower the risk of having a baby that is underweight. Increased low birth weight and infant death were a result of the mother's low BMI. Low birth weight had been more common in moms who were underweight. According to the research, birth weight among the various castes had been associated to rising BMI and maternal weight growth during pregnancy. As per the World Health Organization and United Nations Children's Fund,96% of LBW births, are caused by low socioeconomic situations, poor diet, infections, and physical labour during pregnancy –52.Evidences has shown that inadequate antenatal care (ANC) visits can have significant implications for the course of pregnancy and the health of new born. Studies have also found a strong association between LBW and inadequate ANC visits during pregnancy, which increases the likelihood of having a baby with LBW –55. NotaLess than 5% of the mothers in the current research used cigarettes, compared to more than 50% of the fathers. There is dearth of evidence to suggest that smoking during pregnancy is statistically significantly associated with an increased odds ratio of delivering a low birth weight (LBW) baby. However, Kramer discovered that indoor smoke, cigarette smoking, and tobacco chewing were potentially significant and that their causal influence was proven in his meta-analysis . Based oThis study utilized a representative sample of women in India, obtained from nationally representative data, which allowed us to conduct an analysis that is representative of the country as a whole, and to provide a comprehensive understanding of LBW. Recall bias and reporting errors might be associated particularly with the LBW, and other variables, such as age, education level etc.The determinants of LBW in India were evaluated in this study. The study's findings on LBW predictors can be utilised to both pinpoint high-risk individuals and forecast LBW trends. Further research is required to assess the possible causative impact of various indicators detected during pregnancy, such as BMI, anaemia, smoking, alcohol use, history of LBW, and others. The relationship between mothers age and LBW among new born in India was also examined in this study. According to the study findings, adolescent mothers are more likely to deliver children who have LBW. This evidence can inform intervention strategies for healthcare workers, providers, NGOs, policymakers, and public health professionals in India. In addition to that, there is a need of setting up a portal for LBW as soon as the children is born, their weight should be recorded and should be under supervision up to five years of age to prevent under-5 mortality. Similarly, the home-based neonatal care set-ups should be different for LBW child and child with normal weight. Awareness campaign and behaviour change communication strategies need to be revised for LBW child.Additional file 1:Supplementary 1. Distribution of low birth weight among Indian states and its Union Territories of the recent child born in healthcare facilities (NFHS-5). Supplementary 2. Univariate Logistic Regression of maternal correlates with LBW <2500grams as outcome variable among the recent most child delivered in healthcare facilities of India.


Women with ADHD symptoms compared to those without were significantly younger but had no different cardiological profile. To protect women’s health, further research and multidisciplinary cooperation is required to better understand the relationship between ADHD and cardiovascular disease.Patients with Attention Deficit Hyperactivity Disorder (ADHD) are at greater cardiovascular risk. We investigated the association between ADHD symptoms and cardiovascular disease in women at a specialized Dutch cardiological clinic. Lifetime ADHD symptoms were found in 35% of women ( The worldwide prevalence of ADHD is estimated to be around 5.9% in youth and 2.5% in adults  to screen women with cardiovascular complaints on ADHD symptoms and 2) to assess whether ADHD symptoms in women is associated with an atypical cardiovascular profile.In this retrospective cross-sectional study, clinical data was drawn from electronic health records of all female patients who applied to Heartlife Clinics between May 2021 and May 2022. Heartlife Clinics, a cardiological outpatient clinic in Utrecht, The Netherlands provides cardiovascular care focusing on both the prevention and treatment of heart disease with specialized expertise in women. It is estimated that women comprise 85% of the patients, with the largest age group between 40 and 60 years. Female patients aged 18 years and above with and without a history of cardiovascular disease, who were referred to Heartlife Clinics by their general practitioner or specialist were eligible for inclusion. Patients were asked to complete a baseline questionnaire including the Ultra-short questionnaire for ADHD prior to their first appointment.Data collected from electronic health records included demographic information self-reported lifestyle related risk factors, history of diabetes, cardiac symptoms, physical examination, laboratory blood measurements, resting ECG, exercise stress test, carotid ultrasound measurements, and the diagnosis stable angina. Resting ECG was categorized as normal and abnormal. Examination of the carotid arteries was performed by an experienced cardiologist. The carotid ultrasound measurements were categorized as normal, abnormal carotid intima media thickening (thickened CIMT), and presence of one or more carotid plaques.Patients were screened for lifetime ADHD symptoms using the Ultra-short questionnaire for ADHD Kooij . It covep = 0.043). When corrected for the difference in age, ADHD + women were less likely to be a never drinker compared to ADHD- women . When corrected for age, fasting glucose levels were significantly lower in the ADHD + group compared with the ADHD-group (p = 0.033). None of the other outcomes showed significant differences between the ADHD + women and ADHD- women. ADHD symptoms were nosignificant predictor for atherosclerosis (p = 0.920) nor CMD (p = 0.403)  screened positive, which is a 7 to tenfold increase compared to the prevalence of 3 to 5% in the general population. This high percentage of ADHD symptoms is adds to the previous findings of a prevalence of CVD of 38% in adults with ADHD in a recent population based Swedish Registry study  . However, in this sample we cannot confirm this hypothesis.To the best of our knowledge, this is the first study to examine the the prevalence of ADHD symptoms in women with cardiovascular health concerns in a clinical sample. In this study we did not include control women without cardiovascular problems, but we know the prevalence of ADHD is around 3–5% in the general population, in both males and females. Our study has some limitations. First, we included patients from a cardiac outpatient clinic with a special focus on women’s heart health rather than general cardiac care, therefore, in order to control for potential bias, screening for ADHD symptoms should be replicated in other cardiac centers Second, UKV was chosen because it is the shortest screener available, which is preferred by both patients and specialists in clinical practice, that measures the early onset and lifetime persistence of the three DSM-5 core symptoms of ADHD. Further, UKV is a screener but not a diagnostic tool, which may have overestimated the prevalence of ADHD symptoms in the present sample. Also, we were not able to fully correct for comorbidities (psychiatric disorders like depression), family history of CVD, female specific risk factors, and medication use that might influence the association between ADHD and CVD.Our results suggest added value of ADHD screening in women who are referred for cardiovascular examination. This high prevalence of ADHD symptoms in women at this cardiac clinic points to the need for more research, and alertness from both clinicians and the general public. Both CVD and ADHD symptoms in women are underdiagnosed and undertreated, and this combination is highly detrimental for the health outcomes of these women. It is crucial to improve the quality of life and achieve both prevention of cardiac risks, and optimal, combined cardiac and mental healthcare for women with ADHD, such as the Head, Heart and Hormones (H3) Network in the Netherlands.Ultrashort Questionnaire for ADHD in Adults Do you usually feel restless?1.  Yes/noDo you usually act first and then think?2. Y Yes/noDo you usually have concentration problems?3. Yes/noIf the answer to questions 1 and/or 2 and/or 3 is yes:Have you always had this? 4. Yes/noIf the answer to question 4 is yes, please consider further diagnostic assessment for ADHD with the Diagnostic Interview for ADHD in adults (DIVA-5) (see www.divacenter.eu).Source: JJS Kooij. Adult ADHD. Diagnostic Assessment and Treatment, 4th ed. Springer, 2022.


Seven of the fifteen CDS symptoms showed a good convergent (high loadings on the CDS factor) and discriminant (higher loadings on the CDS factor than the ADHD-IN factor) validity. CDS also showed stronger first-order and unique associations with depression than ADHD-IN, whereas ADHD-IN showed stronger first-order and unique associations with ADHD-HI and anxiety than CDS. The first-order and unique associations of CDS and ADHD-IN did not differ in relation to stress. This study is the first to support the validity of the self-report of assessing CDS symptoms with the Adult Concentration Inventory with Farsi-speaking individuals residing in Iran, thus further strengthening the transcultural validity of the CDS, and paving the way for further transcultural research in the field of CDS among adults.The internal and external validity of cognitive disengagement syndrome (CDS) relative to attention-deficit/hyperactivity disorder-inattention (ADHD-IN) was evaluated herein with Farsi-speaking adults. A total of 837 Iranian adults assessed throughout the whole country (54.72% women, M In recent years, a growing body of research has focused on cognitive disengagement syndrome (CDS), formerly sluggish cognitive tempo (SCT), which refers to a set of attentional symptoms that includes daydreaming, staring, mental fogginess/confusion, and slowed behavior/thinking . Note thAs regards the internal and external validity of 15 frequently-used CDS items for children and adolescents, the parent and teacher rating-scale studies from a variety of countries have shown satisfactory psychometric properties, in that the 15 CDS items loaded differently on the CDS and ADHD-IN factors  validity of the 15 CDS symptoms with the 9 ADHD-IN symptoms. As noted earlier, these 15 CDS symptoms have demonstrated convergent and discriminant validity with the ADHD-IN symptoms from parent and teacher ratings of the 15 CDS and 9 ADHD symptoms obtained from numerous countries including Iran; however, the CDS symptoms with convergent and discriminant validity relative to the ADHD-IN symptoms within the self-report measure have been inconsistent across studies. The first objective was therefore to further attempt to identify the best CDS symptoms on the self-report measures. The identification of CDS symptoms with a good internal validity relative to ADHD-IN symptoms on the self-report measures would then allow for the determination of the external correlates of the CDS and ADHD-IN symptom dimensions. The second objective was to evaluate the invariance of the CDS symptoms across men and women. It was expected that the CDS symptoms would show an invariance of like-item loadings and thresholds across men and women with no significant difference in the CDS factor mean. Given that the studies on the 15 CDS symptoms with the parent and teacher rating scales found an invariance of like-item loadings and thresholds across boys and girls ,8,10,14,The third and fourth objectives evaluated the external validity of the CDS and ADHD-IN factors with other symptom factors . It was expected that CDS would have a stronger correlation and unique association  with depression compared with ADHD-IN, whereas ADHD-IN would have a stronger correlation and unique association than CDS with ADHD-HI ,13,14,15We hold that for the following three reasons, the present validation study is important. First, Farsi is the official language of Iran, Afghanistan, and Tajikistan . FurtherSD 7.05; age range: 18 years to 58 years). Of these, 75% were between 18 and 24 years old; 16% were between 25 and 35 years, and 9% were between 36 and 58 years old, with 54.7% female and 24% married. Educational degrees were reported: diploma (33.9%), bachelor’s degree (53.6%), master’s degree (9.1%), and doctoral degree (3.3%).Participants were recruited from provinces of each region of Iran including the north (Mazandaran), south (Shiraz), east (Kermanshah), west (Mashhad), and center (Tehran and Esfahan). Participants were recruited via advertisements on the intranet of universities, private companies, and sports and leisure time activity associations, including associations for people in retirement. The inclusion criteria were: (i) age between 18 and 65 years; (ii) willing and able to complete a series of self-rating questionnaires in Farsi; (iii) signed written informed consent. The exclusion criteria were: (i) current self-reported psychological, psychiatric, and/or somatic issues, which might have biased the results of the questionnaires; (ii) pregnant or breast-feeding women, as these two conditions might modify the current cognitive-emotional processing. A total of 868 people completed the questionnaire with 31 participants (3.57%) excluded for self-reported current psychological, psychiatric, or somatic issues. The final sample consisted of 837 individuals with a mean age of 23.58 years  approved the study , which was performed in accordance with the seventh and current revision  of the DTo accurately translate the English version of the ACI into Farsi, we followed the algorithms proposed by Brislin , Beaton,The ACI is a self-report measure of 16 CDS symptoms, although one item assessing low motivation has consistently shown a poor convergent and discriminant validity  and was The Farsi version  of the BI felt down hearted and blue), anxiety , and stress . Each item was rated on a four-point scale . For the current sample, the Cronbach’s alpha values for the depression, anxiety, and stress scales were 0.87, 0.82, and 0.83, respectively. Scores from the depression, anxiety, and stress scales have shown positive psychometrics in earlier research [The Farsi version  of the Dresearch ,58.The Mplus statistical software was used for the analyses . The seThe first analysis applied an a priori CDS and ADHD-IN two-factor model to the 15 CDS items and the 9 ADHD-IN items. Items were allowed to have cross-loadings . The puThe second analysis determined the correlations of the CDS and ADHD-IN factors with the ADHD-HI, depression, anxiety, and stress factors. For this six-factor model, the CDS items were allowed to cross-load on the ADHD-IN factor and the ADHD-IN items to cross-load on the CDS factor. No other cross-loadings were allowed in this factor model . The purThe third analysis regressed the ADHD-HI, depression, anxiety, and stress factors on the CDS and ADHD-IN factors. This analysis also allowed the CDS items to cross-load on the ADHD-IN factor and the ADHD-IN items to cross-load on the CDS factor with no other cross-loadings allowed in the model . The puI am slow at doing things; (2) My mind feels like it is in a fog; (4) I feel sleepy or drowsy during the day; (7) I get lost in my own thoughts; (8) I get tired easily; (9) I forget what I was going to say; and (11) I zone or space out. These seven items were retained to define the CDS construct. Cronbach’s alpha for these items was 0.83.χ2 (89) = 266, p < 0.001, CFI = 0.979, RMSEA = 0.049 , and SRMR = 0.028. The average loading of the seven CDS items on the CDS factor was 0.64 (SD = 0.11), with the average loading of the CDS items on the ADHD-factor being 0.08 (SD = 0.12). The average loading of the nine ADHD-IN symptoms on the ADHD-IN factor was 0.67 (SD = 0.10), with the average loading on the CDS factor being 0.02 (SD = 0.13). The seven CDS items thus showed a good convergent and discriminant validity with the nine ADHD-IN items, also showing a good convergent and discriminant validity for the sample. The correlation between the CDS and ADHD-IN factors was 0.69. The CDS and ADHD-IN factors thus maintained 52% of their true score variance independent of the other factor.A CDS and ADHD-IN factor model applied to the seven CDS and nine ADHD-IN items with cross-loadings yielded a close fit: χ2 (48) = 99, p < 0.001, CFI = 0.987, RMSEA = 0.051 , and SRMR = 0.037. The model with the constraints on like-item loadings and like-item thresholds did not result in a significant decrement in fit relative to the model without the constraints: χ2 (20) = 18.89, p = 0.45. Although women had a slightly higher CDS factor mean than men, the difference was not significant: Cohen’s latent d = 0.08, SE = 0.08, p = 0.29.A one-factor model with constraints on like-item loadings and like-item thresholds across men and women yielded an acceptable fit: χ2 (960), 3063, p < 0.001, CFI = 0.928, RMSEA = 0.051 , and SRMR = 0.055. The CDS factor had a stronger correlation than the ADHD-IN factor with the depression factor (p < 0.001), whereas the ADHD-IN factor had a stronger correlation than the CDS factor with the ADHD-HI and anxiety factors (p < 0.001). The CDS and ADHD-IN factors did not differ in their association with the stress factor (p > 0.10).p < 0.001) than the ADHD-IN factor, whereas the ADHD-IN factor had a stronger unique association than the CDS factor with the ADHD-HI (p < 0.001) and anxiety (p < 0.01) factors. The CDS and ADHD-IN factors did not differ significantly in their unique associations with the stress factor (p > 0.10). In addition, the unique associations of the CDS and ADHD-IN factors with the other symptom factors remained the same after controlling for participants’ age, sex, and marital status.The purpose of the study was to evaluate for the first time the construct validity of scores from the 15-item CDS self-report scale with Iranian adults. More specifically, four objectives were formulated. The first objective was to determine the internal  validity of the 15 CDS symptoms with the 9 ADHD-IN symptoms. The second objective was to evaluate the invariance of the CDS symptoms across men and women, and the third and fourth objectives evaluated the external validity of the CDS and ADHD-IN factors with other symptom factors . Results evidenced that 7 of the 15 CDS symptoms showed a good convergent (high loadings on the CDS factor) and discriminant (higher loadings on the CDS factor than the ADHD-IN factor) validity. These seven CDS symptoms showed an invariance of like-item loadings and thresholds across men and women, with no significant difference in the CDS factor mean. Further, CDS also showed stronger first-order and unique associations than ADHD-IN with depression, whereas ADHD-IN showed stronger first-order and unique associations than CDS with ADHD-HI and anxiety. The first-order and unique associations of CDS and ADHD-IN did not differ with stress.The current study is the first to support the validity of the self-report of CDS symptoms with adults from Iran, thus further strengthening the transcultural validity of CDS . In thisThe results showed  that a sQuestions might arise as to what extent the identification of 7 out of 15 items might be considered a satisfactory pattern of results, when compared to the ratio of 9 out of 16 items , or 10 oThe seven CDS symptoms showed an invariance of like-item loadings and thresholds across men and women with no significant difference in the CDS factor mean. In our opinion, this result is important for the following reasons. While prevalence rates and possible gender differences in CDS among children, adolescents, and adults are modest at most , it is iThe overall pattern of results showed that higher scores for CDS were associated with higher scores for symptoms of depression. As such, the present data corroborated what has already been observed ,38,39,63As regards the observed associations between higher scores for CDS and higher scores for stress, it appears that this is only the second study in this field. Among 983 adults aged on average 45.6 years, higher scores for ADHD predicted higher scores for self-perceived stress, while the combination of symptoms of ADHD-IN and symptoms of CDS was the most consistent predictor of perceived stress . The preNext, the associations between CDS and anxiety demand particular attention. Higher scores for ADHD-IN were associated more strongly than scores for CDS with higher scores for anxiety, which appears to contrast with previous studies on CDS and anxiety ,38,39,63The present validation study has the following limitations. First, previous studies have shown that (adult) CDS traits are associated with more sleep difficulties ,34,36; tSecond, we assessed an explicitly non-clinical sample of typically developed adults consisting prevalently of participants in their young adulthood and with a solid higher educational background. In contrast, while the prevalence rate of CDS among adults appears to be unclear, the prevalence of ADHD among adults ranges between 0.23% and 2.18% among older adults , and betThe Farsi version of the Adult Concentration Inventory (ACI) to identify CDS traits among adults is a valid and psychometrically sound measure. Further, the measure might allow for a further and more thorough exploration as to whether adults with ADHD might benefit from a deeper investigation of CDS, particularly in determining treatment routes. Froehlich, Becker, Nick, Brinkman, Stein, Peugh, and Epstein  have sho


The most common psychiatric disorder in childhood is Attention-deficit/hyperactivity disorder (ADHD). Researchers have studied the effects of micronutrients on ADHD in recent years, but vitamin D (vit D) deficiency has received less attention. In this study, serum vit D levels were compared between healthy and ADHD children.This case-control study was carried out, in 2020, on 6-to-12-years-old children. There were 45 children with ADHD in the case group and 45 healthy children in the control group. Intravenous blood samples were taken from each child to measure serum vitamin D levels. A p-value < 0.05 was considered as significant.Mean serum vit D levels in children with ADHD (17.34±8.37 ng / ml) were significantly lower than those in the control group (23.02±10.97 ng / ml) (P= 0.007). There were no significant differences in mean serum levels of vit D due to ADHD subtypes. Mean serum vit D levels were not significantly associated with the gender of children with ADHD. There was an inverse correlation between vit D levels and the severity of ADHD, but it was not statistically significant.The present study showed that children with ADHD had significantly lower serum vit D levels than healthy controls. Attention-deficit/hyperactivity disorder (ADHD) is an early-onset chronic nervous system-related disorder characterized by attention deficit, hyperactivity, and impulsivity . It affeVit D deficiency has been linked to anxiety, mood, and developmental disorders in some studies –15. Vit There are several mechanisms by which vit D supplements reduce the symptoms of ADHD. Vit D is essential for the production of the neurotransmitters dopamine and norepinephrine, which alleviate the symptoms of ADHD . Vit D hThere are many problems and costs associated with ADHD for children, families, and communities, which is why it is important to examine the underlying factors that contribute to its occurrence and development. As vit D is one of the most important nutrients for brain growth and development, its deficiency may contribute to ADHD pathogenesis, but in recent years, micronutrients have been studied for their effects on ADHD, but vit D has received less attention, so this study aimed to compare vitamin D levels between ADHD and healthy children.Study Population: In this cross-sectional analytical study, 90 children aged 6-12 years were selected as study samples at the Pediatric and Adolescent Psychiatric Clinic at Yahyanejad Hospital in Babol. Inclusion criteria included consent to participate in the study and age 6-12, and exclusion criteria were any malabsorption syndrome, neurological disorders such as seizures and metabolic disorders, serious psychiatric disorders such as mood and psychotic disorders, kidney disease, liver disease and a recent history of vit D supplementation. Children with ADHD medical records who attended a pediatric psychiatric clinic at Shahid Yahyanejad hospital were included in the case group (n=45) and non-ADHD children referred to the Babol Social Security clinic for weight and height checks were included in the control group (n=45).Data Collection: Connors Parent Questionnaire and clinical interview with an adolescent and pediatric psychiatrist were used in this study to diagnose ADHD . Vit D serum level of all participants was measured in the summer season. Informed consent was obtained from each participant’s parent, and 5 milliliters (ml) of venous blood were drawn from each child in the control group and the cases group, and 25-hydroxyvitamin D levels were determined in a laboratory at Shafa Babol using the Euroimmun kit with specifications (EXP: 2020/11/14 and lot number: E 191202 BO). A pathologist interpreted the results. Serum vit D levels are classified into four categories: normal (30–100 ng/ml), deficiency (10–29 ng/ml), severe deficiency (10 ng/ml), and toxic (> 100 ng/ml) , 23.Data Analysis: The data were analyzed using SPSS V.22. Chi-square, t-tests, and Pearson correlation coefficients were used. A p-value of less than 0.05 was considered significant.In the present study, 90 children with inclusion criteria (45 in the control group and 45 in the case group) participated. In this study, 59 (65.6%) of the participants were males, and 31 (34.4%) were females. The mean age of the children surveyed was 8.63 ± 1.83 years (range 6-12 years).There was no significant difference between the two groups in terms of age (P=0.121) and gender (P=0.267). The mean serum vit D levels of the children who participated in the study were 20.18 ± 10.11 (1.30-57.5) ng/ml. As shown in It was found in the present study that children in the ADHD (93.33%) group were more likely to lack vit D than their normal counterparts (66.66%). According to a study of 1331 ADHD patients and the same number of controls aged under 18, only 8.15% of ADHD patients had normal serum vit D levels . In anotIn our study, serum vit D levels were significantly lower in the case group than in the control  group. The mean serum vit D levels of children aged 7 to 18 years were significantly different between cases (20.9±19.4 ng/ml) and control groups 34.9±15.4 ng/ml) in a similar study conducted in Turkey . Accordi.4 ng/ml Additionally, our results indicated that there was not any significant relationship between the mean serum levels of vit D and the subtype of ADHD, but there was a statistically insignificant relation between vit D levels and the severity of ADHD, as serum levels of vit D were lower in patients with more severe diseases. The presence of 25(OH) D3-hydroxylase in the brain (which is responsible for the active form of vit D) as well as vit D receptors  support As a result of the lack of significant differences in terms of age and gender between the two groups, this study has been used to eliminate bias in its results. Also, to avoid vit D bias related to sun exposure, samples were collected from June to the end of September 2020. One of the limitations of this study, is the lack of investigation and removal of the confounding factor of the amount of daily exposure to sunlight and children's BMI for a more accurate examination of the vit D serum level. In addition, this study cannot fully explain the cause-and-effect relationship due to its cross-sectional nature. In summery children with ADHD have low levels of serum vit D, suggesting a need to monitor their levels and treat patients with vit D deficiencies. A modified lifestyle and diet are also important to eliminate the nutritional deficiencies in the society.


Attention-deficit/hyperactivity disorder (ADHD) was originally treated as a neurodevelopmental disorder that occurs mainly in children and tends to diminish or disappear with age, but we now know that symptoms persist into adulthood in over 50% of ADHD patients. Undiagnosed individuals often turn to psychoactive substance to minimize the negative aspects of functioning and improve quality of life.The study was conducted online using random sampling through a Facebook group administered by physicians and targeted to patients diagnosed with ADHD. The study was naturalistic and exploratory, therefore no hypothesis was made. 438 correctly completed questionnaires were received. Analysis of the results showed that people with ADHD turn to psychoactive substances relatively frequently.The most commonly used stimulants include alcohol, marijuana, 3,4-methylenedioxymethamphetamine (MDMA), amphetamine/methamphetamine, and psilocybin. In the study population, methylphenidate is the most commonly used drug among patients. After treatment with psychostimulants, the majority of respondents note a decrease in symptoms of hyperactivity disorder, especially in male patients.It is necessary to perform proper diagnostics and actively look for ADHD symptoms in patients who tend to use psychoactive substances. Attention-deficit/hyperactivity disorder (ADHD) was originally considered a neurodevelopmental disorder that occurs mainly in children and subsides or disappears with age. However, it is now known that in a significant proportion of individuals with ADHD, symptoms persist into adulthood , 2. SympStudies have shown that, in contrast to children, adults suffering from ADHD have fewer symptoms from the hyperactivity and impulsivity spectrum, while symptoms from the inattention group seem to predominate . In adulOther disorders may also coexist with attention deficit hyperactivity disorder; studies have shown that up to 80% of adults diagnosed with ADHD may be affected, with the most commonly diagnosed disorders being substance abuse, mood and anxiety disorders, and dissocial personality . Not onlPsychostimulants, especially amphetamine derivatives or methylphenidate, are most commonly used to treat attention deficit hyperactivity disorder, whereas atomoxetine is used when there are existing contraindications to the use of psychostimulants or when it is poorly tolerated. For stimulant abuse, methylphenidate in the form of OROS is the preferred choice, while atomoxetine is preferred for alcohol abusers. The next treatment option described in the studies is bupropion, which is not approved by the Food and Drug Administration (FDA) for the treatment of ADHD . Of noteThe aim of our study was to collect baseline demographic-epidemiological data on psychoactive substance use in patients diagnosed with ADHD and to determine whether adults struggling with ADHD have a greater predisposition to psychoactive substance use and whether and how treatment affects stimulant use.The study was designed by psychiatrists and conducted in accordance with the Declaration of Helsinki and good clinical practice guidelines. Because the study was naturalistic, we did not conduct any pilot study. According to the Ethical Committee of Medical University of Silesia anonymous, internet surveys that do not allow to deanonymize respondents do not require approval. A separate questionnaire was created to survey the population, which included basic sociodemographic questions and several other questions related to diagnoses or treatments for attention deficit hyperactivity disorder, among other factors such as psychoactive substance of choice. The list of psychoactive substance was based on PolDrugs, the biggest Polish naturalistic epidemiological psychoactive users survey for psychoactive users . In addiThe survey was conducted online, using convenience sampling through an online Facebook group that was created for people who were interested in ADHD, managed by physicians. Facebook uses cookies to suggest it’s users certain groups, therefore if people tried to search for ADHD in their search engines, our group could be suggested by Facebook. The group was created about 15 months before the study was conducted, and the group had 5,609 members on the day we started collecting the data . The platform used to created the survey was Google Forms, an easy-to-use tool for creating anonymous online surveys. The researchers did not ask for email addresses or IP addresses, and all data collected were processed in accordance with the data protection regulations of the Republic of Poland’ and the European Union’. Because the survey was an internet one, the consent was also web based and the respondents could not move to the survey without accepting the terms. Because the survey’ is about the possession and use of often illegal psychoactive substances, anonymity was critical to obtaining truthful responses from respondents. Data were collected between October 13, 2022 and October 24, 2022.Participation in the study was voluntary, patients were informed of the purpose of the study, and consent was required in the form of an affirmative response to the question “I am at least 18 years of age and consent to participate in this anonymous study” in order to exclude individuals who merely suspected ADHD in themselves, respondents were also required to confirm in the questionnaire that they had received an ADHD diagnosis from a physician. Only after agreeing to participate was it possible to take part in the survey. A total of 438 completed questionnaires were collected from people who declared having ADHD diagnosed by a physician. The survey was constructed in a way that did not allow user to submit incomplete data however we cannot estimate how many people resigned from filling the survey while completing the survey.The study was an exploratory and naturalistic study, i.e., no research hypothesis was established and the inclusion criteria were limited to a correctly completed questionnaire, but only sought to investigate and characterize the phenomenon of psychoactive substance use and the effects of appropriately selected medications on the reduction of this behavior in a population of adults with attention deficit hyperactivity disorder.Statistical analysis was performed using the STATISTICA 13.3 program . The chi-square test was used for the comparison of qualitative variables.For quantitative variables, the Mann–Whitney U test was used for the comparison of two independent groups, whereas the Kruskal-Wallis test was used for the comparison of multiple independent groups.Spearman’s rank correlation was used to test the correlation of linear variables.p < 0.05.To evaluate the parameters affecting AUDIT and the DUDIT scale, we performed univariate and multivariate linear regression analysis. The significance level was set at K. no significant differences were found between the genders in terms of age, education, place of residence, type of ADHD, or past drug treatment. There is a statistically significant difference in the distribution of sexual orientations between the 2 sexes, consistent with the proportions observed in our other studies of university students . Similarly, there is a difference in work situation between genders .Four hundred thirty-eight correctly completed questionnaires were received. The characteristics of the study population are shown in post hoc analysis, a significant difference in age of ADHD diagnosis was found between respondents who self-identified as “other” gender and gender M or K .In The characteristics of pharmacotherapy used by the respondents are shown in The vast majority of respondents (69%) use only one medication, and the most common is methylphenidate (75%). We did not collect information about methylphenidate formula. No statistically significant differences were found between genders and medications taken. Only 3% of patients discontinued medication due to side effects.p = 0.057 = NS; chi-square test).A reduction in ADHD symptoms after treatment was noted in 83% of respondents, although the difference between genders is borderline and could prove statistically significant with a larger sample .Up to 35% of respondents showed a reduction in psychoactive substance use since starting treatment, which was significantly more common in men .A summary of psychoactive substance use is described in 2 = 5.2; p = 0.022; chi-square test) and likewise opioids . Otherwise, there were no gender differences.The most frequently used psychoactive substances were again THC-containing products (37%), 3,4-methylenedioxymethamphetamine (MDMA) (14%), and amphetamine/methamphetamine and psilocybin (10% each). Males with ADHD were more likely than females to use MDMA (χp > 0.05).16% of respondents are characterized by risky or harmful alcohol consumption, while 5% meet the criteria for the likelihood of alcohol dependence.p < 0.00001; chi-square test).37% of respondents exhibit drug-related problems, according to DUDIT, while approximately 2% are highly likely to be drug dependent. A statistically significant gender difference is found regarding classification into risk groups for drug abuse based on the DUDIT scale . The earlier the age at ADHD diagnosis, the higher the DUDIT score . A similar relationship is observed between age of diagnosis and level of psychoactive substance use . No such correlation is observed for the AUDIT scale .An inverse relationship is observed between age and the amount of psychoactive substance taken equals the lower the age, the greater the amount of psychoactive substance taken .A correlation is observed between the AUDIT scale score and the DUDIT scale . A significant difference was found between the groups with and without drug treatment on both AUDIT and DUDIT .p < 0.05 and 2 clinically significant-gender and type of ADHD-were included in the multivariate analysis, and as a result, the only significant factor conditioning AUDIT was found to be rehab history. The results are presented in Univariate and multivariate linear regression analyses were performed for the AUDIT and DUDIT scales. In the univariate analysis for AUDIT, the variables age and rehabilitation history were found to be statistically significant. The variables from the univariate analysis with p < 0.05 and 1 clinically significant-gender-were included in the multivariate analysis. In the multivariate model, 2 variables were found to be statistically significant-the type of ADHD and history of rehospitalization. The results are presented in In the univariate analysis for DUDIT, the variables ADHD type, age at ADHD diagnosis, use of methylphenidate, and rehab history were found to be statistically significant. Variables from the univariate analysis with Attention deficit hyperactivity disorder (ADHD) is a disorder that affects many areas of human life. Among other things, people with ADHD show a greater tendency to engage in risky behaviors, such as gambling, they tend to leave their jobs abruptly and spend their money recklessly, and they have a higher risk of physical injury or suicide in accidents . Meta-anIn a study we conducted, methylphenidate was found to be the most commonly used medication among people with ADHD in both males and females. This drug is also the most thoroughly studied and has a proven efficacy benefit: symptoms improve shortly after ingestion, and the effect lasts until metabolism in the body is complete . It alsoA significant proportion of respondents to our survey indicated that substance abuse had decreased since treatment began, particularly in the male group, which appears to be important information in the context of conducting appropriate diagnostic testing and treatment engagement. The results of previous studies on the impact of appropriate treatment on reducing psychoactive substance use remain mixed. In part, it has been suggested that treatment use is neither protective nor beneficial for the development of psychoactive substance use disorders later in life . On the The validated DUDIT scale shows that a significant percentage of respondents are highly likely to have a substance abuse problem, and a small percentage are highly likely to exhibit characteristics of substance dependence. It is worth noting that despite the fact that divergent results can be found in the literature, a significant number of meta-analyses indicate that childhood pharmacotherapy with simulants is associated with a reduction in the risk of later use of alcohol and psychoactive substances . ApproprParticipants in our survey confirmed that they were most likely to use products containing THC, MDMA, amphetamine, and psilocybin. Men were significantly more likely than women to admit to using MDMA and opioids, whereas the differences for the other psychoactive substance were not significant. A 2016 study found that women are statistically more likely to become addicted to marijuana, as well as alcohol . It is iOn the AUDIT scale, we observed a tendency toward risky alcohol consumption among the respondents, but also some of the respondents met the criteria for alcohol dependence. Previous studies have concluded that a higher AUDIT score is significantly correlated not only with previously diagnosed ADHD, but also with comorbid schizophrenia and major depressive disorder . In addiIn a study to determine the average age at which ADHD symptoms are diagnosed and appropriate diagnosis and treatment are provided, it was suggested that accurate and appropriate early selection of pharmacotherapy will help prevent the consequences associated with the disorder and reduce society’s costs for the complications of ADHD . This isNo difference was found between ADHD types in respondents on the AUDIT and DUDIT scales. Again, the data are mixed, as over the years it has been shown that the different subtypes do not have a clear impact on substance abuse or that the complex ADHD subtype is associated with more severe effects of psychoactive substance use and more frequent help seeking compared to the predominantly inattentive or predominantly hyperactive/impulsive subtype .It is crucial to highlight the fact that only 38.80% of physicians asked about psychoactive substance use during the treatment. It is important for physicians to discuss psychoactive substance use with their patients during treatment to ensure patient safety and effective medical management – by understanding a patient’s substance use, physicians can identify potential interactions and adjust treatment plans accordingly. Additionally, discussing substance use opens the door for early detection and intervention for substance use disorders, promoting holistic care and improved patient outcomes. It is of utmost importance to properly diagnose and recognize characteristics that may indicate addiction development in patients, as people burdened by mental illness who use psychoactive substance have the highest mortality rate among other diagnoses , 40.People with ADHD use psychoactive substances relatively frequently, and the most commonly used psychoactive substance include alcohol, marijuana, MDMA, amphetamine/methamphetamine, and psilocybin. In the study population, methylphenidate is the drug most commonly used by patients. After treatment with psychoactive substance, there is a decrease in attention deficit hyperactivity disorder symptoms in most respondents, especially in male patients. It is important to perform proper diagnostics in patients prone to psychoactive substance use and to proactively screen for symptoms of ADHD, as properly applied treatment can help reduce troublesome symptoms.The survey was distributed online without identifying respondents, so it is not possible to assess long-term substance use in the context of reducing psychoactive substance use. Because of the way the survey was distributed, it was not possible to calculate response rates. Because the survey was conducted over the Internet, respondents’ answers may not have been entirely reliable, but this was unavoidable given the topic of the survey. There was no way to verify the truthfulness of the answers given. The survey was exploratory in nature, and the topic needs to be further researched, as it seems important to make an accurate diagnosis and choose reasonable treatment at a time when ADHD is becoming more widely known, in order to minimize the potential health, social, and economic consequences of an untreated disorder. Because the study was naturalistic, there was no hypothesis and study conducted without a hypothesis lacks a clear research direction, which can lead to unfocused data collection and analysis. Some of the limitations lay also in the statistical methods used, as univariate linear regression analysis is limited in capturing the complexities of real-world relationships involving multiple predictors and assumes a linear relationship between a single predictor and the dependent variable. Multivariate linear regression analysis assumes no multicollinearity between predictors, and if present, it can lead to unstable coefficient estimates and difficulties in interpreting the relationships accurately. Both univariate and multivariate linear regression analyses may not account for non-linear relationships, outliers, or other underlying factors that can affect the accuracy and reliability of the regression model.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Online written informed consent for participation was obtained from the participants.GW: conceptualization, methodology, visualization, investigation, data curation, and writing—reviewing and editing. IS and MS: software, investigation, data curation, and writing—original draft preparation. WW: supervision, data curation, and investigation. PG: supervision and funding acquisition. TG: visualization, data curation, investigation, and writing—reviewing and editing. All authors contributed to the article and approved the submitted version.GW received honoraria for lectures from: Angelini Pharma Polska and support for attending meetings from: Angelini Pharma Polska, Apotex Poland/Aurovitas Pharma Polska, Lek-AM. IS received support for attending meetings from: Lek-AM. TG received honoraria for lectures from: Valeant Polska, Lundbeck Poland, Apotex Poland/Aurovitas Pharma Polska, Celon Pharma and support for attending meetings from: Lundbeck Poland, EGIS, GL Pharma.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.