The Kids’ Environment and Health Cohort: a novel administrative data resource for research on the environmental determinants of child health in England
Bibliographic record
Abstract
Objective and ApproachThe environment in and around children’s homes and schools can influence their health and educational outcomes. Better understanding of how these potentially modifiable environmental risk factors can affect children is crucial in enabling the creation of healthier and more equitable places. We aim to establish the Kids’ Environment and Health Cohort, a research-ready, de-identified, national longitudinal birth cohort of approximately 11 million children born in England from 2006 to 2023, updated annually. The cohort will link vital statistics, census, health, education, and environmental data, via unique property identifiers from longitudinal health service address records for children and their mothers during pregnancy. Data on environmental exposures around schools will be linked to the cohort via education records. The cohort will be held and accessed in a secure research environment at the Office for National Statistics (ONS). All geographical identifiers will be encrypted and stored separately from the main cohort by the ONS to ensure privacy and security. ResultsWe have received ethics approval and have agreed the legal bases for establishing the cohort. We are now setting up data sharing agreements with each data provider. Delivery of the cohort is scheduled for late 2025. ConclusionThe Kid’s Environment and Health Cohort will support policy-relevant research in exploring associations between environmental factors and children's health and educational outcomes, and assessing the effectiveness of policy interventions. It will also support interdisciplinary collaboration, guiding evidence-based decision-making for environmental, planning, and public health policies aimed at promoting children’s health and well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".