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Introduction: A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills. Objectives: The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys. Methods: The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children’s food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables. Results: The dashboard layout is structured by the YAML (YAML Ain’t Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny’s render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/. Examples of how to interact with the dashboard for selected oral health variables are illustrated. Conclusion: Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

The increasing importance of identifying lesbian, gay, bisexual and transgender (LGBT+) populations is a key driver in changes to demographic data collection in representative surveys of youth. While such population-based data are rare, Growing Up in Ireland (GUI), an Irish, government-funded, longitudinal survey, includes sexual orientation and gender identity (SOGI) measurements. This qualitative study responds to a query from the GUI study team and aims to identify how best to collect SOGI data in future waves of GUI. A university Human Research Ethics Committee granted approval for online consultations with LGBT+ youth (n = 6) with experiential expertise in policy making. The research is underpinned by rights-based public patient involvement (PPI) with recorded discussions, which were transcribed and imported into NVivo 12, generating the theme “recognition in research, policy and society”. This co-created article, with the LGBT+ young PPI Panel members, commends the inclusion of SOGI data in GUI and recommends changes in question placement and phrasing. Aligning with best practice, the PPI members provide a template for wording on consecutive sex and gender questions, expanded sexual orientation identity categories and maintaining the existing well-phrased transgender question from GUI. This offers potential to improve the quality of the SOGI data collected and the experience of those completing the questionnaire. These findings extend beyond GUI, with relevance for surveys with youth populations. This paper underscores the potential and benefits of participatory approaches to research with youth and views their role beyond simply as sources of data.

Keywords: LGBT+, sexual minority youth (SMY), gender minority youth (GMY), Growing Up in Ireland (GUI), SOGI measurement, quantitative, qualitative, survey design, PPI

The increasing emphasis on inclusive education internationally has broadened the definition of special educational needs (SEN) and greatly affected national prevalence estimates. In line with these international trends, in Ireland the EPSEN Act (2004) defines SEN as any “restriction in the capacity of a person to participate in or benefit from education”. Taking this broad definition, this study draws on the first longitudinal study of children in Ireland, Growing Up in Ireland, to generate a new estimate of SEN prevalence among Irish nine-year-old children. The analysis combines detailed information, collected from parents and teachers, encompassing diverse types of SEN, including physical disabilities, speech impairments, learning disabilities and emotional/behavioural difficulties. In doing so, the study establishes a SEN prevalence rate of 25 per cent among children in the mid-primary years, a rate very much in line with recent research in other European contexts. Additionally, the study details the diversity of data collected on children and young people with SEN and disabilities across agencies and government departments, the potential value of this data and directions for improved learner databases.

Within categories of special educational needs, emotional and behavioural difficulties have received much attention in recent years, particularly in relation to their definition and identification by parents and teachers. This paper stems from previous research which highlights how children from disadvantaged backgrounds and those attending schools designated as socio-economically disadvantaged are significantly more likely than their peers to be identified as having a special educational need of a non-normative type such as emotional behavioural difficulty (EBD). Using data from the Growing Up in Ireland study, it examines whether the EBD identified by teachers or within certain schools is matched by the child’s own performance on an internationally validated emotional and mental health measure – the Piers–Harris. Findings show that overall self-reported social emotional well-being bears a strong relationship to the probability of being identified with an EBD. However, boys, children from economically inactive and one-parent households and children attending the most disadvantaged school contexts are more likely to be identified with having an EBD, even after taking into account their social background characteristics and their scoring on the Piers–Harris measure. These findings suggest that the subjective nature of EBD identification is resulting in a disproportionate number of these children being identified with EBD. The implications of this study are explored for existing disability/SEN classification systems, school-wide intervention models and the impact on individual students labelled as EBD. Overall, the findings pose searching questions about the validity of employing SEN classification systems in deciding eligibility and types of appropriate provision.

Despite the growing body of literature on how digital technologies impact child well-being, previous research has provided little evidence on recent digital trends. This paper examines the patterns and effects of digital use on child socioemotional well-being across two cohorts of children grown up ten years apart during the ‘digital age’: the 1998 cohort (interviewed in 2007/08) and the 2008 cohort (interviewed in 2017/18). Multivariate linear regression models were conducted for these two cohorts from the Growing Up in Ireland (GUI) study, a multi-cohort longitudinal study with rich comparable data on a large sample of 9-year olds (N = 13,203). Results show that (i) in 2017/18 children were more active in digital devices and social media, while in 2007/2008 children spent more time watching TV and adopted less diversified forms of media engagement; (ii) spending more than 3 daily hours on TV/digital activities was associated with significant declines in child socioemotional well-being, while such effects were stronger in 2017/18 than in 2007/08; (iii) media engagement (but not other forms of digital engagement) was associated with moderate declines in socioemotional well-being, both in 2007/08 and in 2017/18; (iv) while children’s media and digital engagement differed by the child gender and socioeconomic background, none of these variables moderated the effects of digital use on children’s socioemotional well-being, neither in 2007/08 nor in 2017/18. Overall, the study reveals persistence, but also some important changes, in recent trends on children’s digital use and its impact on socioemotional well-being in Ireland.

Background
There is growing concern around youth mental health. A population health approach to improve mental health must address, among other issues, economic insecurity, access to housing and education, harm reduction from substance use. As a universal public health intervention, increasing physical activity at a population level may have an important role in our approach. The aim of this study was to examine the longitudinal association between physical activity patterns between childhood and early adolescence and emotional-behavioural difficulties in later adolescence.

Methods
This study was based on data from the ′98 Child cohort of the Growing Up in Ireland Study. Participants were categorized according to physical activity levels at ages 9 and 13. Emotional-behavioural difficulties at age 17 were measured using the parent-reported Strengths and Difficulties Questionnaire. Logistic regression was used to examine the association between physical activity and emotional-behavioural outcomes.

Results
Among 4618 participants included in the regression model, those categorized as Inactive (n=1607) or Reducer (n=1662) were more than twice as likely to have emotional-behavioural difficulties at age 17 compared with those who were Active [adjusted odds ratio (AOR) 2.1, 95% CI 1.46–3.01, P<0.001; AOR 1.93, 95% CI 1.34–2.76, P<0.001, respectively]. Among those with emotional-behavioural difficulties at baseline (n=525), those categorized as Active had 2.3-fold reduced odds for emotional-behavioural problems at age 17 compared with those who were Inactive (AOR 0.43, 95% CI 0.23–0.78, P=0.006).

Conclusions
Increasing physical activity among adolescents is a safe and sustainable public health intervention associated with improved mental health.

The first 1000 days of life are a period of unique sensitivity and plasticity during which critical cognitive abilities are formed. Routine developmental screening tools aim to identify infants who would benefit from early intervention. While these tools have been validated for detecting children with more severe neurodevelopmental disorders, their ability to identify the larger proportion with below average cognitive function has not been sufficiently explored. The aim of this study was to examine the validity of the Ages and Stages Questionnaire (ASQ), for identifying children with later below average cognitive function.

The study population (n=8260) is formed from two national cohort studies, the Growing Up in Ireland (GUI) Infant cohort (n=7,444) and the Cork BASELINE cohort (n=816). The ASQ was completed at 8 months and 24-27 months respectively. Cognitive assessments were performed at age 5. Those scoring <1 standard deviation (SD) below the mean were categorised as below average cognitive function. Applying the currently used onward referral criterion (one fail in any domain) the sensitivity, specificity, positive and negative predictive values of the 8- and 24-27- month ASQ for detecting children with later below average cognitive function were calculated.

In the GUI cohort n=905 participants (12.5%) had scores <1SD below the mean on the Picture Similarities Scale. In the BASELINE cohort n=101 participants (13.4%) had an IQ <1SD below the cohort mean. Applying the currently used onward referral criterion (failing in any one domain in the ASQ), the sensitivity of the 8-month ASQ for detecting children scoring <1SD below the mean on the Picture Similarities Scale at age 5 was 16.4% (95% CI 14.0-19.0). The specificity was 92.0% (95% CI 91.3-92.6), with a positive predictive value (PPV) of 22.6% (95% CI 19.5-26.0) and a negative predictive value (NPV) of 88.5% (95% CI 87.7%-89.2%).

In the BASELINE cohort n=468 participants completed the 24-month ASQ and n=316 the 27-month ASQ. Applying the same onward referral criterion to the 24- and 27- month ASQ combined, the sensitivity for detecting those with an IQ <1SD below the cohort mean was 20.8% (95% CI 13.6-30.2) and the specificity was 91.1% (95% CI 88.6-93.2).

The ASQ has a low sensitivity for identifying children with below average cognitive function at age 5. The findings of this study suggest that if we are to intervene early in the developmental trajectory for children with below average cognitive function alternative methods of identifying high risk infants are needed.