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Cross-sectional study

Cross-sectional study

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Cross-sectional study

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  1. Cross-sectional study

  2. 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

  3. 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 population in terms of food and nutrient intake and range of other personal and lifestyle characteristics.

  4. 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, Honolulu and the San Francisco Bay area • showed the highest rates among those who had migrated to the United States.

  5. 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.

  6. Limitation of cross-sectional study • It is not possible to say exposure or disease/outcome is cause and which effect.(不能判定因果關係) • 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.

  7. 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.

  8. 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*

  9. 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

  10. 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).

  11. 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.

  12. 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.