Preventing Childhood Obesity - Evidence Policy and Practice.pdf

Chapter 21
diet - consciousness and dietary habits. 13 Recent analyses
of BMI distribution curves in Japanese women
over six decades of surveys showed both decreases
and increases over time, depending on the different
age-groups. 14
Critical e lements of a ppropriate m onitoring
Critical elements of monitoring are:
• Representativeness of the target sample. Selected
samples in towns, regions or even neighborhoods or
social strata can be difficult to interpret in terms of
national prevalence data.
• Size of the sample (i.e. how fine - grained the data are
for analyses by subgroups of age, sex, social class,
etc).
• Frequency of measurements. For some estimate of
secular trends at least three measures are needed
(preferably more). If a survey is done every five years
this implies that 15 years will have passed before any
judgement regarding time trends is possible.
Continuous monitoring systems are, therefore, preferable
also because the continuity of methods is more
easily realized when done continuously compared
to measurements with relative long time intervals.
• Participation rate and selection bias. The characteristics
of non - responders are usually difficult to
determine but the possibilities exist that overweight
and obese children are less likely to participate in
health surveys (particularly when they are also used
for case finding). This may be explained by their
weight status directly but also of variables related to
both overweight and participation such as socio -
economic status.
• Validity of measurements. The validity of selfreported
heights and weights is low, thus leading to
misinterpretation about body composition, fat distribution
and health risks.
Monitoring of ( p otential)
d eterminants and c onsequences
of o besity
Cross - sectional analyses of associations between
potential determinants or consequences of obesity
may lead to interesting hypotheses but usually
inferences about temporal relationships or causation
should be made with caution. There are, however,
examples of evaluations of interventions using
monitoring systems. The interpretations become
easier if, for instance, sharp changes in determinants
(e.g. physical activity or diet) occur in one part of
the population but not in others and are followed by
changes in the health outcome (e.g. obesity prevalence)
in the part of the population where the determinants
changed.
Dietary habits and physical activity patterns may
not only cause weight gain and obesity but obese
people may also change their habits as a result of their
weight status. This, for instance, can produce associations
between dieting and obesity but the causal relationships
are unclear. An example of this can be found
in the study in adolescents in New Zealand by Utter
et al. 15 Examination of the nutritional correlates of
BMI in the total population found inverse relationships
between BMI and consumption of high - fat/
high - sugar foods and positive relationships between
BMI and eating five or more fruits and vegetables a
day (all significant after controlling for age, sex and
ethnicity). For example, students who drank the most
soft drinks or ate fruit and vegetables infrequently had
the lowest mean BMI. Students ’ attempts to change
their weight significantly moderated the relationships
between most nutritional behaviors and BMI. In most
cases, among students not trying to change their
weight, expected relationships were observed; among
students trying to lose weight, unexpected or no relationships
were observed. The authors conclude that
among this population of predominately overweight
students, solely relying on cross - sectional findings
between nutrition behaviors and BMI would misinform
intervention strategies. 15
In addition, other factors may affect associations
between potential determinants and obesity. For
instance, it is frequently observed that, in cross -
sectionally collected survey data, increasing age is
associated with higher BMI and obesity until the age
of about 60 – 65, when it levels off, and even older ages
are associated with lower BMI and obesity levels. This,
as has been argued elsewhere 16 may reflect premature
mortality among obese people but also true decreases
in obesity in old age due to weight loss from illnesses
or age - related loss of appetite. It may also reflect that
older people in a survey have a different life history
(i.e. they may have been born and raised in times
when obesity was much less common than today).
This latter explanation is called a cohort effect. In fact,
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