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.