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

利用英国威尔士的医疗保健、人口统计和社会经济数据预测儿童入学时的超重和肥胖情况

阅读:3

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。