Parental perception of mental health needs in young children

Parental Perception of Children's Mental Health During the Pandemic: Insights From an Italian Cross-Sectional Study

BACKGROUND

This study explores the impact of the pandemic on children's mental health. It examined the understanding of parents regarding their children's mental condition and their ability to identify issues, 2 years post the outbreak of the COVID-19 pandemic.

METHODS

Using a cross-sectional design, 507 Italian parents reported on their youngest child aged between 2 and 17, totaling 507 children. The outcomes focused on were parental perception of children's mental health deterioration, scores on the Strengths and Difficulties Questionnaire (SDQ) above the clinical cut-off, and parental under-recognition of mental health issues. Descriptive analyses and multivariable logistic regression models were executed (significance at p < .05).

RESULTS

Parents were 88.1% women (median age 41 years, interquartile range [IQR] = 36-47). Their children were 50.3% female [median age 6 years (IQR = 4-11)]. The data revealed 21.1% of parents perceived a deterioration in their children's mental health, while 44.2% had SDQ scores above the cut-off. Parental under-recognition of mental issues was found in 20.1% of cases. Significant correlations were found between parental perception of deterioration, SDQ scores, and factors like parental mental distress and children's sleep issues.

IMPLICATIONS

The findings suggest that schools and verified websites can serve as critical conduits for providing parents with reliable information. By promoting early identification and intervention, such mechanisms can help ensure mental health equity for children.

CONCLUSIONS

The research highlights the effect of the pandemic on children's mental health and the issue of parental under-recognition. The results underscore the importance of public health initiatives that enhance mental health information accessibility and reliability for parents.

Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges

In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

Childhood attention-deficit hyperactivity disorder: socioeconomic inequalities in symptoms, impact, diagnosis and medication

BACKGROUND

Children from disadvantaged backgrounds are at greater risk of attention-deficit hyperactivity disorder (ADHD)-related symptoms, being diagnosed with ADHD, and being prescribed ADHD medications. We aimed to examine how inequalities manifest across the 'patient journey', from perceptions of impacts of ADHD symptoms on daily life, to the propensity to seek and receive a diagnosis and treatment.

METHODS

We investigated four 'stages': (1) symptoms, (2) caregiver perception of impact, (3) diagnosis and (4) medication, in two data sets: UK Millennium Cohort Study (MCS, analytic n ~ 9,000), with relevant (parent-reported) information on all four stages (until 14 years); and a population-wide 'administrative cohort', which includes symptoms (child health checks) and prescriptions (dispensing records), born in Scotland, 2010-2012 (analytic n ~ 100,000), until ~6 years. We described inequalities according to maternal occupational status, with percentages and relative indices of inequality (RII).

RESULTS

The prevalence of ADHD symptoms and medication receipt was considerably higher in the least compared to the most advantaged children in the administrative cohort (RIIs of 5.9 [5.5-6.4] and 8.1 [4.2-15.6]) and the MCS (3.08 [2.68-3.55], 3.75 [2.21-6.36]). MCS analyses highlighted complexities between these two stages, however, those from least advantaged backgrounds, with ADHD symptoms, were the least likely to perceive impacts on daily life (15.7% vs. average 19.5%) and to progress from diagnosis to medication (44.1% vs. average 72.5%).

CONCLUSIONS

Despite large inequalities in ADHD symptoms and medication, parents from the least advantaged backgrounds were less likely to report impacts of ADHD symptoms on daily life, and their children were less likely to have received medication postdiagnosis, highlighting how patient journeys differed according to socioeconomic circumstances.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health

Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4-8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70-73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63-82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51-.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.

Mental health needs and accessing specialised healthcare in Mexican children with mental disorders: gender- and diagnosis-dependent differences

BACKGROUND

Access describes factors that influence the initial contact or use of services, emphasising both the characteristics of patients and the health resources that influence the use of health services.

AIMS

To compare Mexican boys and girls with mental disorders, with respect to primary diagnosis, symptom onset, and seeking and accessing specialised mental health services (SMHS).

METHOD

Longitudinal data were collected from primary caregiver-reported assessments of 397 child-caretaker dyads (child mean age 12.17 years, range 5-18 years, 63% male) that were obtained in two psychiatric hospitals specialising in child mental healthcare. Student t-tests and χ2-tests were applied to compare boys and girls regarding their diagnosis and variables associated with the seeking of and access to SMHS.

RESULTS

Hyperkinetic disorder was the most prevalent diagnosis in boys, whereas depressive disorder and anxiety disorder were most prevalent in girls. The mean age at symptom onset for boys was 7 years, compared with 10 years for girls. Hyperkinetic disorder had the earliest symptom onset (mean 5.9 years), followed by depressive disorder (mean 9.8 years) and anxiety disorder (mean 12 years). Delayed access to SMHS was associated with initially seeking care from a psychologist, whereas quicker access was associated with affiliation with the (now defunct) Popular Insurance, a programme that served low-income and uninsured individuals.

CONCLUSIONS

Programmes aimed at children's mental health education and early intervention should consider gender- and diagnosis-related differences in symptom onset and trajectory. Access to SMHS might be improved by rapid identification by parents, educators, primary-care physicians and psychologists.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

Associations of preschool reactive bed-sharing with sociodemographic factors, sleep disturbance, and psychopathology

OBJECTIVE

To advance understanding of early childhood bed-sharing and its clinical significance, we examined reactive bed-sharing rates, sociodemographic correlates, persistence, and concurrent and longitudinal associations with sleep disturbances and psychopathology.

METHODS

Data from a representative cohort of 917 children (mean age 3.8 years) recruited from primary pediatric clinics in a Southeastern city for a preschool anxiety study were used. Sociodemographics and diagnostic classifications for sleep disturbances and psychopathology were obtained using the Preschool Age Psychiatric Assessment (PAPA), a structured diagnostic interview administered to caregivers. A subsample of 187 children was re-assessed approximately 24.7 months after the initial PAPA interview.

RESULTS

Reactive bed-sharing was reported by 38.4% of parents, 22.9% nightly and 15.5% weekly, and declined with age. At follow-up, 48.9% of nightly bed-sharers and 88.7% of weekly bed-sharers were no longer bed-sharing. Sociodemographics associated with nightly bed-sharing were Black and (combined) American Indian, Alaska Native and Asian race and ethnicity, low income and parent education less than high school. Concurrently, bed-sharing nightly was associated with separation anxiety and sleep terrors; bed-sharing weekly was associated with sleep terrors and difficulty staying asleep. No longitudinal associations were found between reactive bed-sharing and sleep disturbances or psychopathology after controlling for sociodemographics, baseline status of the outcome and time between interviews.

CONCLUSIONS

Reactive bed-sharing is relatively common among preschoolers, varies significantly by sociodemographic factors, declines during the preschool years and is more persistent among nightly than weekly bed-sharers. Reactive bed-sharing may be an indicator of sleep disturbances and/or anxiety but there is no evidence that bed-sharing is an antecedent or consequence of sleep disturbances or psychopathology.

Commentary: Timely recognition of mental health needs in young children - parental perception as a way for professionals to understand child, parent, and family needs? - a commentary on McGinnis et al. (2021)

There is a growing awareness that identification and intervention of mental health problems in early childhood are effective and have a preventive effect. Nevertheless, the use of mental health services in early childhood is still limited. Investigating parental perception of mental health needs in young children is highly relevant, as parents are the primary informants for young children. In this commentary, we discuss the findings reported by McGinnis et al. on child and parent factors associated with parent perception of mental health needs in young children. We agree with their plea for a shift from the individual child to the family in assessment and intervention, and elaborate on the relevance of endorsing a transdiagnostic, relationship-based approach to early detection, assessment, and intervention. We propose beginning with the acknowledgment and deeper understanding by professionals of parental perception of young children's mental health problems and needs, and the realization that the young child's symptoms and needs are connected with the family and broader (relational) context.