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BackgroundResearch and clinical practice rely heavily on caregiver-report measures, such as the Child Behavior Checklist 1.5-5 (CBCL/1.5-5), to gather information about early childhood behavior problems and to screen for child psychopathology. While studies have shown that demographic variables influence caregiver ratings of behavior problems, the extent to which the CBCL/1.5-5 functions equivalently at the item level across diverse samples is unknown.MethodsItem-level data of CBCL/1.5-5 from a large sample of young children (N = 9087) were drawn from 26 cohorts in the Environmental influences on Child Health Outcomes program. Factor analyses and the alignment method were applied to examine measurement invariance (MI) and differential item functioning (DIF) across child (age, sex, bilingual status, and neurodevelopmental disorders), and caregiver (sex, education level, household income level, depression, and language version administered) characteristics. Child race was examined in sensitivity analyses.ResultsItems with the most impactful DIF across child and caregiver groupings were identified for Internalizing, Externalizing, and Total Problems. The robust item sets, excluding the high DIF items, showed good reliability and high correlation with the original Internalizing and Total Problems scales, with lower reliability for Externalizing. Language version of CBCL administration, education level and sex of the caregiver respondent showed the most significant impact on MI, followed by child age. Sensitivity analyses revealed that child race has a unique impact on DIF over and above socioeconomic status.ConclusionsThe CBCL/1.5-5, a caregiver-report measure of early childhood behavior problems, showed bias across demographic groups. Robust item sets with less DIF can measure Internalizing and Total Problems equally as well as the full item sets, with slightly lower reliability for Externalizing, and can be crosswalked to the metric of the full item set, enabling calculation of normed T scores based on more robust item sets.

AbstractPurposeMethodsResultsConclusionsTo access the attitudes of service users about the sharing of health records for research and to foster collaboration between municipal health services and the specialist health services in Norway.Members (n ≈ 2000) of the Norwegian mental health service users’ organizations (SUO’s), ADHD Norway, the Autism Association and the Tourette Association, representing Central Norway, participated in the study, (N = 108, 5.4% response rate). Descriptive statistics were used to evaluate distributions of responses to the questionnaire.Service users reported being aware that municipal health services collaborate with the specialist health service (62%), with mental health care in the specialist health service (57%), and child and adolescent psychiatric services (61%). A large proportion of individuals were aware of the benefits of sharing their health records (93%), have trust in the use of data by health authorities (81%), and were willing to share records to benefit fellow patients (84%). Personal experience (69%) and impressions from mainstream media (55%) had the most influential impact on users’ views of the Health Platform, an electronic health communication system. A majority of users had a negative perception of the Health Platform, even though some expect it to become a valuable tool in the future (50%).Service users are aware of and positive about benefiting others by sharing health records. They trust the health authorities, however, have negative attitudes about the Health Platform, apparently based on personal experiences and media influence. However, service users can see the potential usefulness of the Health Platform in the future. [ABSTRACT FROM AUTHOR]

Background: Sleep problems are reported for up to 80% of autistic individuals. We examined whether parsimonious sets of items derived from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and the Brief Infant Sleep Questionnaire (BISQ) are superior to the standard M-CHAT-R in predicting subsequent autism spectrum disorder (ASD) diagnoses., Methods: Participants from 11 Environmental influences on Child Health Outcomes (ECHO) cohorts were included. We performed logistic LASSO regression models with 10-fold cross-validation to identify whether a combination of items derived from the M-CHAT-R and BISQ are superior to the standard M-CHAT-R in predicting ASD diagnoses., Results: The final sample comprised 1552 children. The standard M-CHAT-R had a sensitivity of 44% (95% CI: 34, 55), specificity of 92% (95% CI: 91, 94), and AUROC of 0.726 (95% CI: 0.663, 0.790). A higher proportion of children with ASD had difficulty falling asleep or resisted bedtime during infancy/toddlerhood. However, LASSO models revealed parental reports of sleep problems did not improve the accuracy of the M-CHAT-R in predicting ASD diagnosis., Conclusion: While children with ASD had higher rates of sleep problems during infancy/toddlerhood, there was no improvement in ASD developmental screening through the incorporation of parent-report sleep metrics., Impact: Parental-reported sleep problems are common in autism spectrum disorder (ASD). We investigated whether the inclusion of parental-reports of infant/toddler sleep patterns enhanced the effectiveness of developmental screening for autism. We reported higher rates of difficulty falling asleep and resisting bedtime during infancy and toddlerhood among children later diagnosed with ASD; however, we did not find an improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. In our sample, the standard M-CHAT-R had a sensitivity of 39% among children of mothers with government insurance compared with a sensitivity of 53% among children of mothers with employer-based insurance., (© 2024. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

The Electronic Health Record system BUPdata served Norwegian Child and Adolescent Mental Health Services (CAMHS) for over 35 years and is still an important source of information for understanding clinical practice. Secondary usage of clinical data enables learning and service quality improvement. We present some insights from explorative data analysis for interpreting the records of patients referred for hyperkinetic disorders. The major challenges were data preparation, pre-analysis, imputation, and validation. We summarize the main characteristics, spot anomalies, and detect errors. The results include observations about the patient referral diversity based on 12 different variables. We modeled the activities in an individual episode of care, described our clinical observations among data, and discussed the challenges of data analysis.