Office of the Children's Commissioner

The Children’s Commissioner, Dame Rachel de Souza, has a statutory remit to investigate the experiences of children in England. It is her duty to promote and protect the rights of all children, with particular regard to children who are living away from home or receiving social care services. In doing this she regularly exercises her power to require government departments and other public bodies to provide administrative data, most of it at child level. The Commissioner’s research team aim to use cutting-edge methods and approaches to analysis of this data, and to triangulate findings with qualitative evidence from visits to settings such as schools, and the experiences of children the office represents directly. This presentation will describe some of the office’s most sensitive and innovative recent and current research. These involve the gathering, linking and analysis of admin data in youth justice, education and children’s social care. Examples include novel analysis published 2022 of 512 Education Health and Care Plans; text analysis of over 13 million words of police interview transcripts conducted with children, published 2023; and novel linkage of education and social care data, published 2023. The policy context and impact of the studies will be discussed, as well as methodological challenges and opportunities for future research by the office and in partnership with other researchers

Beyond Screen Time: Assessing Recreational Sedentary Behavior among Adolescent Girls

Most studies of sedentary behavior have focused on television use or screen time. This study aims to examine adolescent girls' participation in a variety of recreational sedentary behaviors (e.g., talking on the phone and hanging around), and their association with physical activity (PA), dietary behaviors, and body mass index. Data were from a sample of 283 adolescent girls. Recreational sedentary behavior, PA, and dietary behaviors were self-reported, and girls' height and weight were measured. Over 95% of girls engaged in at least one recreational sedentary behavior during the recall period. Watching television and hanging around were the most common behaviors. Watching television, using the Internet, and hanging around were associated with less PA; watching television, hanging around, and talking on the phone were associated with less healthful dietary behaviors. No associations were found with body mass index. Interventions may benefit from capitalizing on and intervening upon girls' common recreational sedentary behaviors

Understanding the impact of professional motivation on the workforce crisis in medicine: a rapid review

Background: The NHS is facing a workforce crisis. Responses to date have focused on improving recruitment of staff, but less attention has been paid to retention. Aim: To conduct a rapid review using Rosabeth Moss Kanter's three Ms model of workforce motivation as a sensitising framework to examine the current medical workforce crisis. The work considers how insights from research in other professions offers new thinking for understanding what motivates doctors to continue working. Design & setting: Rapid literature review with secondary analysis of existing research examining reasons for leaving medicine. Method: A systematic search strategy was developed with the aid of an information specialist. The search terms used were: medical professionals, retention, and NHS. The exclusions were: commentaries, non-medical professionals, non-English language, and it was limited to post-1990. The search was applied to three electronic databases, MEDLINE, Embase, and Healthcare Management Information Consortium (HMIC). This produced a dataset describing study design, and factors related to motivation for leaving the medical profession. Comparative thematic analysis distilled core themes explaining the reasons for leaving and their relation to the three Ms model. Results: Of 3389 abstracts identified, screening and assessment produced 82 articles included in the final analysis. Thematic analysis identified four key themes: low morale, disconnect, unmanageable change, and lack of personal and professional support. The themes of mastery, membership, and meaning were substantially present within the dataset. Conclusion: Kanter's three Ms model of motivation can be applied to the medical workforce to understand retention issues. This work supports the development of targeted solutions to tackle the worsening workforce crisis

Perylene-Based Covalent Organic Frameworks for Acid Vapor Sensing.

Traditionally, the properties and functions of covalent organic frameworks (COFs) are defined by their constituting building blocks, while the chemical bonds that connect the individual subunits have not attracted much attention as functional components of the final material. We have developed a new series of dual-pore perylene-based COFs and demonstrated that their imine bonds can be protonated reversibly, causing significant protonation-induced color shifts toward the near-infrared, while the structure and crystallinity of the frameworks are fully retained. Thin films of these COFs are highly sensitive colorimetric acid vapor sensors with a detection limit as low as 35 μg L-1 and a response range of at least 4 orders of magnitude. Since the acidochromism in our COFs is a cooperative phenomenon based on electronically coupled imines, the COFs can be used to determine simultaneously the concentration and protonation strength of nonaqueous acid solutions, in which pH electrodes are not applicable, and to distinguish between different acids. Including the imine bonds as function-determining constituents of the framework provides an additional handle for constructing multifunctional COFs and extending the range of their possible applications

The Delta Study - Prevalence and characteristics of mood disorders in 924 individuals with low mood: Results of the of the World Health Organization Composite International Diagnostic Interview (CIDI).

OBJECTIVES: The Delta Study was undertaken to improve the diagnosis of mood disorders in individuals presenting with low mood. The current study aimed to estimate the prevalence and explore the characteristics of mood disorders in participants of the Delta Study, and discuss their implications for clinical practice. METHODS: Individuals with low mood (Patients Health Questionnaire-9 score ≥5) and either no previous mood disorder diagnosis (baseline low mood group, n = 429), a recent (≤5 years) clinical diagnosis of MDD (baseline MDD group, n = 441) or a previous clinical diagnosis of BD (established BD group, n = 54), were recruited online. Self-reported demographic and clinical data were collected through an extensive online mental health questionnaire and mood disorder diagnoses were determined with the World Health Organization Composite International Diagnostic Interview (CIDI). RESULTS: The prevalence of BD and MDD in the baseline low mood group was 24% and 36%, respectively. The prevalence of BD among individuals with a recent diagnosis of MDD was 31%. Participants with BD in both baseline low mood and baseline MDD groups were characterized by a younger age at onset of the first low mood episode, more severe depressive symptoms and lower wellbeing, relative to the MDD or low mood groups. Approximately half the individuals with BD diagnosed as MDD (49%) had experienced (hypo)manic symptoms prior to being diagnosed with MDD. CONCLUSIONS: The current results confirm high under- and misdiagnosis rates of mood disorders in individuals presenting with low mood, potentially leading to worsening of symptoms and decreased well-being, and indicate the need for improved mental health triage in primary care

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score >= 5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD