{
  "abstract": "Background In Wales, 11.4% of children aged 4–5 years live with obesity, with the issue being more pronounced for those living in poorer areas. Weight status tracks from childhood to adolescence to adulthood, and obesity is linked to developing multiple long-term conditions (MLTCs). There is no early identification system during pregnancy or in early life to detect those at high risk of childhood obesity. We have previously developed childhood obesity prediction models using healthcare data in Hampshire, externally validated in Bradford. Pregnancy predictors included maternal body mass index (BMI), smoking, age and ethnicity, and early life predictors included birthweight, child sex and weight at 1 or 2 years. In this study, we aimed to predict childhood obesity using healthcare and wider demographic, socioeconomic and area-level data in Wales as part of the MELD-B project examining the role of early life factors in relation to later MLTCs.Methods The Secure Anonymised Information Linkage (SAIL) Databank in Wales contains routinely-collected individual-level anonymised data from health records and administrative data (census and birth registrations). We linked singleton births between 15thMarch 2010 and 28th March 2012 (to enable inclusion of Census 2011 data) to mother’s pregnancy records. Age- and sex-adjusted BMI at 4–5 years was used to define outcome of overweight and obesity using the UK clinical cut-off of ≥91st centile. Backward stepwise logistic regression models with multivariable fractional polynomials were used to develop the models in two stages – first using clinical factors and then adding census and area-level factors.Results Data was available on 53,815 children at 4–5 years. Variables retained in the first stage included maternal factors: age, BMI, smoking, parity, ethnic group, marital status, anaemia, venous thromboembolism, and child factors: birthweight, gestational age at birth, gender and breastfeeding at birth. Additional variables retained in the model on adding census and area-level factors included: unpaid carer, maternal educational attainment, type of area of residence (urban/rural) of mother during pregnancy, and Welsh Index of Multiple Deprivation of child’s residence. Discrimination (Area Under the Curve) improved from 0.66 with clinical predictors to 0.67 with wider factors.Conclusion Clinical factors retained were largely consistent with existing literature. Additional insights were provided by the inclusion of census and area-level characteristics though the increase in model discrimination was marginal. Childhood obesity can act as a mediator on the pathway to MLTCs, and risk identification tools may be beneficial to target early prevention.",
  "authors": [
    {
      "affiliations": [
        "School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK"
      ],
      "name": "Nida Ziauddeen"
    },
    {
      "affiliations": [
        "School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK",
        "NIHR Applied Research Collaboration Wessex, Southampton, UK"
      ],
      "name": "Simon DS Fraser"
    },
    {
      "affiliations": [
        "School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK",
        "NIHR Applied Research Collaboration Wessex, Southampton, UK"
      ],
      "name": "Sebastian Stannard"
    },
    {
      "affiliations": [
        "School of Economic, Social and Political Sciences, University of Southampton, Southampton, UK"
      ],
      "name": "Ann Berrington"
    },
    {
      "affiliations": [
        "Population Data Science, Swansea University Medical School, Faculty of Medicine, Swansea University, Swansea, UK"
      ],
      "name": "Roberta Chiovoloni"
    },
    {
      "affiliations": [
        "Population Data Science, Swansea University Medical School, Faculty of Medicine, Swansea University, Swansea, UK"
      ],
      "name": "Ashley Akbari"
    },
    {
      "affiliations": [
        "Population Data Science, Swansea University Medical School, Faculty of Medicine, Swansea University, Swansea, UK"
      ],
      "name": "Rhiannon K Owen"
    },
    {
      "affiliations": [
        "School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK"
      ],
      "name": "Shantini Paranjothy"
    },
    {
      "affiliations": [
        "School of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University of Southampton, Southampton, UK",
        "NIHR Applied Research Collaboration Wessex, Southampton, UK",
        "University Hospital Southampton NHS Foundation Trust, Southampton, UK"
      ],
      "name": "Nisreen A Alwan"
    }
  ],
  "title": "P76 Predicting childhood overweight and obesity using maternal and early life healthcare and administrative data in Wales, UK",
  "uid": "002f5d28-c48e-505c-a58b-15f72c06c4a0"
}