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
Objectives We aim to establish the Kids’ Environment and Health Cohort, a research-ready, de-identified, longitudinal birth cohort of approximately 11 million children born in England (2006-2022), updated annually. The cohort will be used to investigate how environmental factors in and around children’s homes and schools affect their health and educational outcomes. Method The Kids’ Environment and Health 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. Environmental exposure data in/around schools will be linked via education records. The Office for National Statistics (ONS) is developing Phase 1, which includes birth and death registrations (2006-2022), linked deterministically using a combination of NHS numbers and personal information. Cohort children born within two years of the 2011 or 2021 Census will be linked to their mother’s Census record. Environmental data on air pollution, greenspace proximity, temperature, and building characteristics will be linked to all cohort children via birth addresses. The cohort will be held and accessed in a secure research environment at the ONS, with encrypted geographical identifiers stored separately to ensure privacy. Results We have received ethics approval and agreed the legal bases for establishing the Kids’ Environment and Health Cohort. The linkage of death registrations to birth registrations is complete, and the ONS team were able to match >97% of registered deaths to birth records of cohort children with both precision and recall estimated at >99%. Researchers will be able to request access to the Phase 1 data via ONS by March 2026. Conclusion The 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 to promote children’s health and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".