2026-03-19
Nick Bowden, Francesca Anns, Sally Clendon, Joanne Dacombe, Lisa Meehan, Hien Vu, Emma Woodford, Laurie McLay
Introduction Participation in education underpins positive lifelong outcomes, yet Autistic children often encounter barriers to enrolment, attendance, and access to support. Evidence indicates that systemic challenges such as inadequate support, limited autism-specific teacher training, and restricted access to resources contribute to disparities in educational outcomes. While small sample studies highlight these inequities, population-level evidence is limited. Objectives To quantify nationwide differences in school enrolment, attendance, and access to educational resourcing and support services between Autistic and non-Autistic children aged 5--12 years in Aotearoa New Zealand (NZ), and to examine variation by co-occurring intellectual disability (ID). Methods Cross-sectional analysis using NZ's Integrated Data Infrastructure, including all children aged 5--12 in 2019. Autism and ID were identified from hospital, mental health, and disability service use datasets. Outcomes included enrolment, attendance, and access to supports. Propensity score matching (1:10) compared Autistic and non-Autistic students across outcomes, including stratification by ID status. Results Among 517,872 students aged 5-12 years, 8,169 (1.6%) were Autistic and of those 28.8% had co-occurring ID. Compared to matched peers, Autistic children were less likely to be enrolled in school (94.9% vs. 97.4%; Prevalence ratio [PR]=0.97, 95% confidence interval [CI]=0.97-0.98) but more likely to be enrolled in specialist schools (14.4% vs. 0.2%; PR=70.15, 95% CI=65.73-74.88), Te Kura (2.1% vs. 0.2%; PR=9.65, 95% CI=8.22-11.34), or home-schooling (2.2% vs. 0.9%; PR=2.45, 95% CI=2.11-2.84). Regular attendance was lower (49.3% vs. 61.2; PR=0.80, 95% CI=0.79-0.82), with higher rates of chronic absence (7.7% vs. 3.2%; PR=2.45, 95% CI=2.27-2.64). Access to supports was significantly higher for Autistic students across a range of services. Disparities were often more pronounced among Autistic children with ID. Conclusion This study demonstrates significant differences in enrolment, attendance, and access to educational supports between Autistic and non-Autistic students in NZ, underscoring the urgent need for targeted and sufficiently resourced supports to ensure equitable participation.
2025-10-06
Alice Kininmonth, Duong Van, Victoria Jenneson, Emma Wilkins, Alison Fildes, Alexandra Johnstone, Michelle Morris
Introduction & Background Supermarket sales data are an exciting and promising type of digital footprint, or smart, data. Their scale and timeliness offer great potential to understand a variety of wicked problems from obesity to climate change. However, like many digital footprint data they do not come research ready and can require subject matter expertise (nutrition) to prepare. Objectives & Approach We outline the data sources and preprocessing stages required to use digital footprint data from 4 large UK retailers to evaluate the impacts of the high in fat, sugar and salt (HFSS) legislation which came into force in England on 1 October 2022. To undertake this evaluation, we first needed to establish the HFSS status of all food and drink products that were sold 18 months pre- and 12 months post-introduction of the legislation. To do this, we obtained daily food and drink purchase records from the 4 UK retailers which included a stock keeping unit identifier, product names and quantity of food purchases. We then obtained food and drink product (nutrition) data from several sources, including retailers’ own-brand data and the third-party commercial data provider, Brandbank, which provided both own-brand and branded data. Product data comprised back-of-pack nutrition information, fruit, vegetable and nut %, product weight, HFSS information (if available), packaging type, Nutrient Profile Model scores. Where available, historic product data from 2020-2024 were obtained to establish any changes to products through reformulation. Data from available sources were aggregated to calculate the HFSS status of all products sold across the 4 retailers. These data were then linked back to sales data to calculate the proportion of HFSS products sold daily in the 4 retailers pre- and post-implementation. Relevance to Digital Footprints Our work outlines the data sources and preprocessing stages required to be able to use digital footprints of food purchases to facilitate the evaluation of the legislation. Conclusions & Implications There are several critical stages required to use digital footprint data for research purposes. This work highlights the need for universally available product data, including historic product data, to enable timely evaluations of public health legislation. Funding Statement This research was funded through the Transforming the UK Food System for Healthy People and a Healthy Environment SPF Programme, delivered by UKRI, in partnership with the Global Food Security Programme, BBSRC, ESRC, MRC, NERC, Defra, DHSC, OHID, Innovate UK and FSA. The DIO Food project is linked to grant award BB/W018021/1 and additionally funded by IGD.
2025-10-06
Lynette Linzbuoy, Mark S Gilthorpe, Alison J Heppenstall, Jiaqi Ge
Introduction & Background Most human beings have an intuitive understanding of causation; however, it is a complex phenomenon which remains largely under-researched. After decades of research and philosophical discussion, there are different formal systems that seek to define what the concepts of causality mean and, importantly, how we might understand causality within practical research applications. To address this, many methods have been developed across different fields. In epidemiology, the main approach relies on the evaluation of counterfactual contrasts via statistical regression models informed by either the theoretical potential outcomes framework or graphical causal models (in the form of a directed acyclic graph, DAG). In recent years, agent-based models (ABMs) have emerged as promising tools for causal inference evaluations within complex systems. Objectives & Approach The aim was to build a causal framework embedding a priori causal structures into synthetic data for robust, causally informed ABMs. Key variables identified in the DAG included age, sex, ethnicity, and comorbidities, which directly influenced infection susceptibility and recovery. Synthetic data were generated based on these predefined causal relationships. These data were embedded within a NetLogo ABM, where agent infection statuses were updated iteratively using logistic regression models grounded in DAG-defined paths. The DAG-informed ABM was compared with a naïve ABM, where transition parameters were selected from established infectious disease models, and a spatially unconstrained microsimulation model (MSM), where transition parameters were derived explicitly from the causally structured synthetic data, but agent-to-agent interaction was not permitted. Relevance to Digital Footprints Through the understanding of individual behaviours, a directed acyclic graph is generated to codify important variables influencing infectious disease prevalence. The synthetic data generated from this enables a realistic simulation of population health patterns, thereby identifying key variables for informed public health interventions. These methods could be applied to digital footprint data of various types. Conclusions & Implications Integrating DAGs with ABMs enhances the robustness of disease transmission simulations. The DAG-informed ABM produced outcomes more closely aligned with expected epidemiological behaviour, such as heterogeneous infection and recovery patterns, including subpopulations that remained uninfected throughout the simulation. Future work will extend the framework to incorporate real-world spatial data and multilevel hierarchical structures.