Post on 29-Dec-2015
Cross-sectional study
1.
Definition
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
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?)
.e.q. The British National Diet and Nutrition Survey or
Nutrition and Health Survey in Taiwan To describe various age groups in the populatio
n 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. e.q. A study of the prevalence (percentage) of coronary
heart disease among men of Japanese ancestry living in Japan, Hon
olulu and the San Francisco Bay area showed the highest rates among those who had migrat
ed to the United States.
KAP (knowledges, attitudes, and practices ) study: 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
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
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: 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 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
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
Before starting any formal analysis, the data should be checked for any errors and outlines.
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.
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.