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Background: A substantial proportion of children have a physical illness; these children commonly experience physical–mental comorbidity. To assess child mental health, brief scales that can be used in clinical and research settings are needed. This study assessed the validity and reliability of parent‐reported Ontario Child Health Study Emotional Behavioural Scale‐Brief Version (OCHS‐EBS‐B) scores. Methods: Data come from a longitudinal study of children aged 2–16 years with a physical illness recruited from outpatient clinics at a pediatric hospital. Confirmatory factor analysis and McDonald's coefficient assessed the factor structure and internal consistency reliability of the OCHS‐EBS‐B, respectively. Point biserial correlations assessed agreement between the OCHS‐EBS‐B and Mini International Neuropsychiatric Interview for Children and Adolescents (MINI‐KID), a structured diagnostic interview. The Wilcoxon rank sum test compared OCHS‐EBS‐B scores between children with versus without physical–mental comorbidity (known‐group validity). Results: The three‐factor structure of the OCHS‐EBS‐B was replicated in this sample of children with physical illness (χ2 = 196.23(272), p < 0.001; CFI = 0.98; TLI = 0.98; SRMR = 0.06; RMSEA [90% CI] = 0.034 [0.027, 0.044]). It had excellent internal consistency reliability (ω = 0.86–0.92) and was moderately correlated with the MINI‐KID (baseline: rpb = 0.43–0.51; 6 months: rpb = 0.55–0.65). OCHS‐EBS‐B scores were significantly higher among children with versus without physical–mental comorbidity. Conclusions: Findings confirm psychometric evidence that the OCHS‐EBS‐B is a valid and reliable measure of mental health in children with chronic physical illness. Its brevity and robust psychometric properties make the OCHS‐EBS‐B a strong candidate for routine use in integrated pediatric physical and mental health services. [ABSTRACT FROM AUTHOR]

Purpose: Because physical-mental comorbidity in children is relatively common, this study tested for response shift (RS) in children with chronic physical illness using a parent-reported measure of child psychopathology. Methods: Data come from Multimorbidity in Children and Youth across Life-course (MY LIFE), a prospective study of n = 263 children aged 2–16 years with physical illness in Canada. Parents provided information on child psychopathology using the Ontario Child Health Study Emotional Behavioral Scales (OCHS-EBS) at baseline and 24 months. Oort's structural equation modeling was used to test for different forms of RS in parent-reported assessments between baseline and 24 months. Model fit was evaluated using root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean residual (SRMR). Results: There were n = 215 (81.7%) children with complete data and were included in this analysis. Of these, n = 105 (48.8%) were female and the mean (SD) age was 9.4 (4.2) years. A two-factor measurement model provided good fit to the data [RMSEA (90% CI) = 0.05 (0.01, 0.10); CFI = 0.99; SRMR = 0.03]. Non-uniform recalibration RS was detected on the conduct disorder subscale of the OCHS-EBS. This RS effect had negligible impact on the longitudinal change in externalizing and internalizing disorders construct over time. Conclusions: Response shift detected on the conduct disorder subscale of the OCHS-EBS, indicated that parents of children with physical illness may recalibrate their responses on child psychopathology over 24 months. Researchers and health professionals should be aware of RS when using the OCHS-EBS to assess child psychopathology over time. [ABSTRACT FROM AUTHOR]

Estimates of longitudinal change in the parameters of latent (i.e. unobserved) variables, including means, are affected by non-response on the items or indicators of the latent variable. This study used Monte Carlo simulation and a numeric example to compare four ordinal item non-response methods: non-negative matrix factorization (NNMF), multiple imputation with conditional proportional odds model (POM), full information maximum likelihood (FIML) and complete-case analysis, when estimating the longitudinal change in latent variable means. The mean squared error for the NNMF method was more than 40% lower than for the FIML and POM methods when the latent variable correlations over time were strong, percentage of missing data was 25% or more, and sample size was large. The NNMF method is a promising method to address item non-response. It is relatively efficient when sample size is large, and the percentage of missing data is high but has limitations under other data-analytic conditions. [ABSTRACT FROM AUTHOR]

Purpose: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods., Methods: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified., Results: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points., Conclusion: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward., (© 2021. The Author(s).)