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Cross-sectional study
Definition
1.
 A cross-sectional studies
 a type of observational or descriptive study
 the research has no control over the exposure
of interest (e.q. diet).
 It involves
 identifying a defined population at a particular
point in time
 measuring a range of variables on an
individual basis
 include past and current dietary intake
Uses of cross-sectional studies
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Prevalence survey: The studies are commonly
used to describe the burden of disease in the
community and its distribution.

Describe population characteristics: They are
also commonly used to describe population
characteristics, often in terms of person (who?)
and place (where?)
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.e.q.
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The British National Diet and Nutrition Survey or
Nutrition and Health Survey in Taiwan

To describe various age groups in the
population in terms of food and nutrient intake
and range of other personal and lifestyle
characteristics.
 Migrant study : Some migrant studies may full into
the classification of cross-sectional studies. These
studies give clues as to association between genetic
background and environmental exposures on the
risk of disease.
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e.q. A study of the prevalence (percentage) of coronary
heart disease
among men of Japanese ancestry living in Japan,
Honolulu and the San Francisco Bay area
showed the highest rates among those who had
migrated to the United States.
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KAP (knowledges, attitudes, and practices ) study:
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KAP studies are purely descriptive and help to build up a
better understanding of the behavior of the population,
without necessarily relating this to any disease or health
outcome.
Management tool: health service managers and
planners may make use of cross-sectional survey to
assess utilization and effectiveness of service.
Development of hypothesis: Hypotheses on the
causes of disease may be developed using data from
cross-sectional study survey.
Limitation of cross-sectional study
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It is not possible to say exposure or
disease/outcome is cause and which effect
Confounding factors may not be equally distributed
between the groups being compared and this
unequal distribution may lead to bias and
subsequent misinterpretation.
Cross-sectional studies within dietary survey, may
measure current diet in a group of people with a
disease. Current diet may be altered by the
presence of disease.
A further limitation of cross-sectional studies may
be due to errors in recall of the exposure and
possibly outcome.
Design of cross-sectional survey
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The problem to be studied must be clearly
described and a thorough literature review
undertaken before starting the data collection.
Specific objectives need to be formulated.
The information has to be collected and data
collection techniques need to be decided.
Sampling is a particularly important issue to
ensure that the objectives can be met in the
most efficient way.

Fieldwork needs planning:
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Who is available to collect the data ?
Do they need training ?
If more than one is to collect the data then it is
necessary to assess between-observer variation.
The collection, coding and entry of data need
planning.
 A pilot study is essential to test the proposed
methods and make any alternations as
necessary.
* The steps are summarized in Fig 13.5*
Dietary assessment in cross-sectional
studies
 Some characteristics of dietary assessment
methods for cross-sectional studies
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Measures an individual’s intake at one point in
time.
Does not require long-term follow up or repeat
measures
Valid
Reproducible
Suitable
Cost within study budget
Dietary method application

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Food records using household measures have
been used in cross-sectional studies.
The recall method attempts to quantify diet over a
defined period in the past usually 24 hours.
The most commonly used dietary assessment
method which attempts to measure usual intake is
the food frequency questionnaire (FFQ).
Analysis of cross-sectional study
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Before starting any formal analysis, the data
should be checked for any errors and
outlines.
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Obvious error must be corrected.
The records of outliners should be examined
excluded
Checking normality of data distribution.

e.q. using the Kolmogorov-Smirnov Goodness
of Fit Test.
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Standard descriptive statistics can then be
used: mean, median, quartiles, and mode;
measure of dispersion or variability such as :
standard deviation; measure precision such
as: standard error, and confidence intervals.
Mean can be compared using t-tests or
analysis of variance (ANOVA).
More complex multivariate analysis can be
carried out such as multiple and logistic
regression.