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Dear all,

On Wednesday 17th March, the RSS Leeds/Bradford local group will
be hosting an afternoon of talks on "Statistics and obesity" featuring
Kimberley Edwards (University of Leeds), Andy Sutherland (NHS IC) and
Claire Griffiths (Leeds Metropolitan University).

The meeting will be held at Leeds Metropolitan University Civic Quarter
(City Site) in the Rose Bowl (Lecture Theatre F) between 2pm and 5pm,
with a break for refreshments around 3.30pm. Refreshments will also be
available from 1.30pm. For directions see:
http://www.leedsmet.ac.uk/metoffice/estateservices/transport/

Further details can be found on our webpage:

http://tinyurl.com/rss-lba

Regards, Paul
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Dr. Paul D. Baxter
Secretary/Treasurer, RSS Leeds/Bradford Local Group,
Division of Biostatistics, University of Leeds, Leeds, LS2 9JT, UK.
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Leeds/Bradford: Wednesday 17 March, 2.00pm, Leeds Metropolitan University.

Statistics and obesity

Andy Sutherland and Paul Eastwood (Information Centre for Health and
Social Care)

The shape of the nation: NHS statistics on obesity

  From surveys measuring Body Mass Index and waist circumference, and
collections on children's height and weight to how obesity is linked to
lifestyle habits, the risks of developing diseases due to obesity and
hospital admissions and prescribing, the NHS has a wealth of information
on this subject.

Kimberley Edwards (University of Leeds)

Spatial microsimulation modeling to estimate small area data: childhood
obesity and the environment

Obesogenic environments are a major explanation for the rapidly
increasing prevalence in obesity. Investigating the relationship between
obesity and obesogenic variables at the micro-level will increase our
understanding about local differences in risk factors for obesity.
Spatial microsimulation models can be used to estimate previously
unknown data at the micro-level. SimObesity is a spatial microsimulation
model designed to create micro-level estimates of obesogenic environment
variables in Leeds: consisting of a plethora of health, environment, and
socio-economic variables. It combines individual micro-data from two
national surveys with a coarse geography, with geographically finer
scaled data from the 2001 Census, using a reweighting deterministic
algorithm. This creates a synthetic population of individuals /
households in Leeds with attributes from both the survey and census
datasets. Logistic regression analyses were used to identify the
covariates that were the strongest predictors of the output variables
and these were used as the model input variables. The model is validated
using linear regression and equal variance t-tests.
Height, weight, age, gender, and residential postcode data were
collected on children aged 3 to 13 years in the Leeds metropolitan area,
and obesity described as above the 98th centile for the British
reference dataset. Geographically weighted regression is used to
investigate the relationship between different obesogenic environments
and childhood obesity. Validation shows that the small area estimates
were robust. The different obesogenic environments, as well as the
parameter estimates from the corresponding local regression analyses,
are mapped, all of which demonstrate non-stationary relationships. These
results show that social capital and poverty are strongly associated
with childhood obesity (p < 0.001).
This paper demonstrates a methodology to estimate health variables at
the small area level. The key to this technique is the choice of the
model`s input variables, which must be predictors for the output
variables; this factor has not been stressed in other spatial
microsimulation work. Validation of these models can be challenging and
both internal and external validation should be undertaken. This paper
also provides further evidence for the existence of obesogenic
environments for children.

Claire Griffiths (Leeds Metropolitan University)

Childhood Obesity: A study of children in Leeds secondary schools

Obesity is one of the most significant public health challenges facing
the UK, with one in three children being affected. As a consequence of
obesity children have an increased risk of developing serious health,
psychological and social problems and are more likely to be obese in
adulthood. The increased health risks translate into an increased burden
on national health care budgets.
Height, weight, age, gender, residential postcode, ethnicity and
educational attainment data were collected on over 14 000 year 7 school
children (aged 11-12) over a three year period (2005-2007) in Leeds.
This presentation seeks to answer the question; what is the probability
of a pupil being classified as overweight or obese, given his or her
characteristics such as age, gender, socioeconomic status, ethnicity and
previous educational attainment.
This presentation employs a multilevel analysis to model overweight and
obesity. Such detailed information could potentially ensure that in the
future Government and public sector agencies provide appropriate levels
of support and target appropriate levels of resources to those most in
need. This has strong implications for clinical and public intervention
and prevention programmes working to achieve local and national targets.

The meeting will be held at Leeds Metropolitan University Civic Quarter
(City Site) in the Rose Bowl (Lecture Theatre F) between 2pm and 5pm,
with a break for refreshments around 3.30pm. Refreshments will also be
available from 1.30pm.

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