Risk of not being in employment, education or training (NEET) in late adolescence is signalled by school readiness measures at 4-5 years.

BACKGROUND: Not being in employment, education, or training (NEET) is associated with poor health (physical and mental) and social exclusion. We investigated whether England's statutory school readiness measure conducted at 4-5 years provides a risk signal for NEET in late adolescence. METHODS: We identified 8,118 individuals with school readiness measures at 4-5 years and NEET records at 16-17 years using Connected Bradford, a bank of linked routinely collected datasets. Children were categorised as 'school ready' if they reached a 'Good Level of Development' on the Early Years Foundation Stage Profile. We used probit regression and structural equation modelling to investigate the relationship between school readiness and NEET status and whether it primarily relates to academic attainment. RESULTS: School readiness was significantly associated with NEET status. A larger proportion of young people who were not school ready were later NEET (11%) compared to those who were school ready (4%). Most of this effect was attributable to shared relationships with academic attainment, but there was also a direct effect. Measures of deprivation and Special Educational Needs were also strong predictors of NEET status. CONCLUSIONS: NEET risk factors occur early in life. School readiness measures could be used as early indicators of risk, with interventions targeted to prevent the long-term physical and mental health problems associated with NEET, especially in disadvantaged areas. Primary schools are therefore well placed to be public health partners in early intervention strategies.

Potential for England's statutory school entry assessment to identify special educational needs and reveal structural inequalities: a population-based study.

OBJECTIVE: To investigate at a population level whether England's universal assessment of 'school readiness' is associated with later identification of special educational needs (SEN). Also, whether ethnic differences exist in SEN identification (white British versus ethnic minority) and whether this varies as a function of school readiness. METHOD: Analysis included 53 229 individuals aged 5-12 years from the Connected Bradford Database (2012/2013-2019/2020). Logistic regression analyses examined: (1) whether reaching a 'good level of development' on England's 'school readiness' assessment was associated with later SEN identification; and (2) whether interactions exist between school readiness and ethnicity. RESULTS: 32 515 of 53 229 children reached a good level of development, of which 3036 (9.3%) were identified as having SEN. In contrast, 10 171 of 20 714 (49.1%) of children who did not reach a good level of development were later identified as having SEN. Children not reaching a good level of development had increased odds of being later identified as having SEN after controlling for covariates (OR: 8.50, 95% CI: 8.10 to 8.91). In children who did not reach a good level of development, white British children had higher odds of being identified as having SEN compared with ethnic minority peers (OR: 1.22, 95% CI: 1.11 to 1.34). No ethnic differences of having SEN were observed in children reaching a good level of development (OR: 1.04, 95% CI: 0.93 to 1.16). CONCLUSIONS: School readiness assessments are associated with later SEN identification. Structural inequalities may exist in SEN identification in children not entering formal education 'school ready'. Such assessments could facilitate earlier identification of SEN and reduce structural inequalities in its identification.

Linking the Urban Environment and Health: An Innovative Methodology for Measuring Individual-Level Environmental Exposures.

Environmental exposures (EE) are increasingly recognised as important determinants of health and well-being. Understanding the influences of EE on health is critical for effective policymaking, but better-quality spatial data is needed. This article outlines the theoretical and technical foundations used for the construction of individual-level environmental exposure measurements for the population of a northern English city, Bradford. The work supports 'Connected Bradford', an entire population database linking health, education, social care, environmental and other local government data over a period of forty years. We argue that our current understanding of environmental effects on health outcomes is limited both by methodological shortcomings in the quantification of the environment and by a lack of consistency in the measurement of built environment features. To address these shortcomings, we measure the environmental exposure for a series of different domains including air quality, greenspace and greenness, public transport, walkability, traffic, buildings and the built form, street centrality, land-use intensity, and food environments as well as indoor dwelling qualities. We utilise general practitioners' historical patient information to identify the precise geolocation and duration of a person's residence. We model a person's local neighbourhood, and the probable routes to key urban functions aggregated across the city. We outline the specific geospatial procedure used to quantify the environmental exposure for each domain and use the example of exposure to fast-food outlets to illustrate the methodological challenges in the creation of city and nationwide environmental exposure databases. The proposed EE measures will enable critical research into the relationship and causal links between the built environment and health, informing planning and policy-making.

Connected Bradford: a Whole System Data Linkage Accelerator.

The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.

Systematic approach to school-based assessments for autism spectrum disorders to reduce inequalities: a feasibility study in 10 primary schools.

OBJECTIVES: This was a pilot study to explore whether the Early Years Foundation Stage Profile (EYFSP) carried out by UK teachers within the 'reception' year, combined with the Social Communication Questionnaire (SCQ), can lead to early identification of children with autism spectrum disorders (ASD) and early access to intervention and can reduce inequity in access to assessment and intervention. DESIGN: Pragmatic prospective cohort. SETTING: Ten primary schools from the SHINE project in Bradford. PARTICIPANTS: 587 pupils from 10 schools who transitioned from reception to year 1 in July 2017 and had the EYFSP completed were included in the final study. INTERVENTIONS: The assessment involved a multidisciplinary team of three staff who completed Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule Version 2, classroom observations with an ASD checklist, a teacher-based ASD questionnaire and a final consensus meeting. PRIMARY OUTCOME MEASURE: National Institute for Health and Care Excellence guideline-compliant clinical diagnosis of ASD. SECONDARY OUTCOME MEASURES: Age of diagnosis, demographic data and feasibility parameters. RESULTS: Children with low scores on the EYFSP were more likely to score above the SCQ threshold of 12, indicating potential autism (50% compared with 19% of children with high scores on the EYFSP (p<0.001)). All children scoring above the SCQ threshold received a full autism assessment; children who scored low on the EYFSP were more likely to be diagnosed with autism (and other developmental issues) compared with those who did not. CONCLUSIONS: We identified nine new children with a diagnosis of ASD, all from ethnic minorities, suggesting that this process may be addressing the inequalities in early diagnosis found in previous studies. All children who scored above the SCQ threshold required support (ie, had a neurodevelopmental disorder), indicating the EYFSP questionnaire captured 'at-risk' children.

Investigating the association between early years foundation stage profile scores and subsequent diagnosis of an autism spectrum disorder: a retrospective study of linked healthcare and education data.

OBJECTIVE: We set out to test whether the early years foundation stage profile (EYFSP) score derived from 17 items assessed by teachers at the end of reception school year had any association with autism spectrum disorder (ASD) diagnosis in subsequent years. This study tested the feasibility of successfully linking education and health data. DESIGN: A retrospective data linkage study. SETTING AND PARTICIPANTS: The Born in Bradford longitudinal cohort of 13, 857 children. OUTCOME MEASURES: We linked the EYFSP score at the end of reception year with subsequent diagnosis of an ASD, using all ASD general practitioner Read codes. We used the total EYFSP score and a subscore consisting of five key items in the EYFSP, prospectively identified using a panel of early years autism experts. RESULTS: This study demonstrated the feasibility of linking education and health data using ASDs as an exemplar. A total of 8,935 children had linked primary care and education data with 20.7% scoring <25 on the total EYFSP and 15.2% scoring <10 on a EYFSP subscore proposed by an expert panel prospectively. The rate of diagnosis of ASDs at follow-up was just under 1% (84 children), children scoring <25 on the total EYFSP had a 4.1% chance of ASD compared with 0.15% of the remaining children. Using the prospectively designed subscore, this difference was greater (6.4% and 0.12%, respectively). CONCLUSIONS: We demonstrate the feasibility of linking education and health data. Performance on teacher ratings taken universally in school reception class can flag children at risk of ASDs. Further research is warranted to explore the utility of EYFSP as an initial screening tool for ASD in early school years.