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 d escribe the burden of disease in the community and its di stribution.
• Describe population characteristics: They are also com monly used to describe population characteristics, often i n terms of person (who?) and place (where?)
e.q.
– The British National Diet and Nutrition Survey, US Nutrition an d Health Examination Survey, or Nutrition and Health Survey in Taiwan
– To describe population as a whole and various age groups in ter ms of food and nutrient intake and range of other personal and li festyle characteristics.
• Migrant study : Some migrant studies may full int o the classification of cross-sectional studies. Thes e studies give clues as to association between gen etic background and environmental exposures on the risk of disease.
e.q. Ni-Hon-San study
– A study of the prevalence (percentage) of coronary hear t disease
– among men of Japanese ancestry living in Japan, Honol ulu and the San Francisco Bay area
– showed the highest rates among those who had migrate d 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, wit hout necessarily relating this to any disease or health outc ome.
• Management tool: health service managers and plan ners may make use of cross-sectional survey to asses s utilization and effectiveness of service.
• Development of hypothesis: Hypotheses on the caus es of disease may be developed using data from cros s-sectional study survey.
Limitation of cross-sectional study
• It is not possible to say exposure and disease/outcome has causal relation
• 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 t ime.
– 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 da
ta should be checked for any errors and ou
tliers.
– Obvious error must be corrected.
– The records of outliers should be examined an d 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.
Matching exercise
• One to one match by sex and by age
• Frequency match by sex group and by age group
– Separate into age-sex groups
• 6age groups and 2 sex group 12 age-sex groups
• Age 1sex1: Case 10 control 10 (show Prof pan the total no available)
• Age2 sex1: case 15 control 15 (show Prof pan the total no a vailable)
Compare three designs
• Selection bias
– Cases and controls
• Dietary or nutrition status
– Information bias
• Disease status
– Ascertainment bias
