Evidence map›Paper›PMID 41974166›Full record

ArticleEuropean journal of public health2026

Predicting childhood overweight and obesity at school entrance using healthcare, demographic, and socioeconomic data in Wales, UK.

Nida Ziauddeen, Simon D S Fraser, Sebastian Stannard, Ann Berrington, Roberta Chiovoloni, Ashley Akbari, Rhiannon K Owen, Nisreen A Alwan

Abstract read
In one paragraph

Article in European journal of public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nida ZiauddeenSchool of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.ORCID 0000-0002-8964-5029
Simon D S FraserSchool of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.ORCID 0000-0002-4172-4406
Sebastian StannardSchool of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.ORCID 0000-0002-6139-1020
Ann BerringtonSchool of Economic, Social and Political Sciences, University of Southampton, Southampton, United Kingdom.
Roberta ChiovoloniPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom.
Ashley AkbariPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom.ORCID 0000-0003-0814-0801
Rhiannon K OwenPopulation Data Science, Swansea University Medical School, Faculty of Medicine, Health & Life Science, Swansea University, Swansea, United Kingdom.
Nisreen A AlwanSchool of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, United Kingdom.ORCID 0000-0002-4134-8463

Funding

Artificial Intelligence for Multiple and Long-Term Conditions NIHR203988National Institute for Health Research (NIHR)NIHR Applied Research Collaboration WessexNIHR or the Department of Health and Social Care
6 · The paper itself

Abstract

In Wales, 24.8% of children aged 4-5 years live with overweight/obesity. Obesity is linked to developing multiple long-term conditions. We aimed to predict childhood obesity using healthcare and wider demographic, socioeconomic, and area-level data. The Secure Anonymized Information Linkage (SAIL) Databank in Wales contains routinely collected individual-level anonymized data from health records and administrative data. Two subsamples were created. The first restricted to singleton births between 15 March 2010 and 28 March 2012 to include Census 2011 data. The second included births after 1 January 2014 to include early-life measurements. Age- and sex-adjusted body mass index (BMI) at 4-5 years was used to define outcome of overweight/obesity (≥91st centile). Backward stepwise logistic regression models with multivariable fractional polynomials were used to develop models in stages. Data were available on 53 815 children at 4-5 years in census and 60 990 children in early-life subsample. Maternal BMI, smoking, marital status, birthweight, ethnic group, gender, and breastfeeding at birth were retained in all models. Additional variables were retained on adding census and area-level factors but increase in discrimination (Area Under the Curve, AUC) was marginal (0.66-0.67). In the second subsample, AUC improved from 0.67 to 0.79 as factors up to weight at 27 months were incorporated. Factors from healthcare records were largely consistent with existing literature. Additional insights were provided by including census data, though increase in model discrimination was marginal. Childhood obesity can act as a mediator on the pathway to multiple long-term conditions, and risk identification tools may target early prevention.

Indexed as

OverweightPediatric ObesityBody Mass IndexChild, PreschoolFemaleHumansMaleRisk FactorsSocioeconomic FactorsWales

Identifiers

PMID41974166
PMCPMC13075943

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.