Measuring the school impact on child obesity  

Oleh:

Procter, Kimberley L. ; Rudolf, Mary C. ; Feltbower, Richard G. ; Levine, Ronnie ; Connor, Anne ; Robinson, Michael ; Clarke, Graham P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Social Science & Medicine (www.elsevier.com/locate/sosscimed) vol. 67 no. 2 (Jul. 2008), page 341.
Topik: UK; Childhood Obesity; School; Resource Allocation; Public health

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: SS53.21 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This article explores the impact that schools have on their pupils' obesity and so identify those where targeted input is most needed. A modelling process was developed using data that had been collected over 2 years on a socio-economically and ethnically representative sample of 2367 school pupils aged 5 and 9 years old attending 35 Leeds primary schools. The three steps in the model involved calculating the “Observed” level of obesity for each school using mean body mass index standard deviation (BMI SDS); adjusting this using ethnicity and census-derived deprivation data to calculate the “Expected” level; and calculating the “Value Added” by each school from differences in obesity at school entry and transfer. We found there was significant variance between the schools in terms of mean BMI SDS (range -0.07 to +0.78). Residential deprivation score and ethnicity accounted for only a small proportion of the variation. Expected levels of obesity therefore differed little from the Observed, but the Value Added step produced very different rankings. As such, there is variation between schools in terms of their levels of obesity. Our modelling process allowed us to identify schools whose levels differed from that expected given the socio-demographic make up of the pupils attending. The Value Added step suggests that there may be a significant school effect. If this is validated in extended studies, the methodology could allow for exploration of mechanisms contributing to the school effect, and identify schools with the highest unexpected prevalence. Resources could then be targeted towards those schools in greatest need.

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