Cross-Sectional Study (prevalence study)

1. Cross-Sectional Study (prevalence study) Ma Jinxiang, M.S. Department of preventive medicine, Guangzhou medical college • 1021311

2. Cross-Sectional Study • Definition • Purpose • Category • Sample size • Content and mode of investigation • Analysis of date

3. Definition of cross-sectional study A cross-sectional study is a descriptive study in which disease and exposure status are measured simultaneously in a given population, It measures the prevalence of health outcomes(also called prevalence study) or determinants of health, or both, in a population at a point in time or over a short period.

4. A cross-sectional studies • a type of observational or descriptive study • the research has no control over the exposure of interest . • 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 exposure

5. Purposes of Cross sectional Studies • 1.They provide clues to disease etiology, and help in the formulation of an etiological hypothesis • 2.They provides background data for planning, organizing and evaluating preventive and curative services by disease surveillance. • 3.They contribute to research by describing variations in disease occurrence by time, place and person • 4.They provide data regarding the magnitude of disease load and types of disease problems in the community in terms of morbidity and mortality rates and ratios.

6. Category of cross sectional study Census(screening) The Convenience Sample Non-random sampling (non-probability sampling) Quota Sampling Sampling Simple random sample Random sampling (probability sampling) Systematic random sample Stratified random sample Cluster sampling Multi-stage sampling

7. category Census A complete study of the population as compared to a sample at a particular point in time Screening Screening is the practice of investigatingapparently healthyindividuals with the object of detecting unrecognised diseaseor its precursors in order that measures can be taken to prevent or delay the development of disease or improve prognosis.

8. category Advantages of Census Surveys • Everyone Has an Opportunity to Participate • Popularize knowledge of medicine and find all cases of disease • Accuracy Concerns are Reduced • Easier to Administer. • Obtains Better Demographic Data Disadvantages of Census Surveys • Higher Cost & More Time

9. category A simple random sample (SRS) A simple random sample gives each member of the population an equalchance of being chosen.  It is not a haphazard sample as some people think!  One way of achieving a simple random sample is to number each element in the sampling frame (e.g. give everyone on the Electoral register a number) and then use random numbers to select the required sample. 

10. category Random numbers can be obtained using your calculator (RAN#), a spreadsheet [The formula =RAND( ) in Excel ], printed tables of random numbers, or traditional methods of drawing slips of paper from a hat, tossing coins or rolling dice. • First step of SRS is that you have a list of the population or you assign every one a number-1,2,3,etc. • SRS is a base of other sampling

11. category A systematic random sample This is random sampling with a system, From the sampling frame, a starting point is chosen at random, and thereafter at regular intervals.

12. For example, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.

13. category A stratified random sample In a stratified sample the sampling frame is divided into non-overlapping groups or strata, e.g. geographical areas, age-groups, genders. A sample is taken from each stratum, and when this sample is a simple random sample it is referred to as stratified random sampling. (proportional allocation )

14. The main advantage of stratified random sample is it’s reduction in sampling error. It ensures better coverage of the populationthan other sampling Note: the difference among individuals in the stratum, the little the better. The difference between the strata, the bigger the better.

15. category Cluster Sampling In cluster sampling the units sampled are chosen in clusters, close to each other. Examples are households in the same street, or successive items off a production line. The population is divided into clusters, and some of these are then chosen at random. • The disadvantage of cluster sampling is it’s larger sampling error

16. category Multi-stage sampling multi-stage sampling starts by dividing the country into a number of regions. Some of these are selected at random and subdivided further, e.g. into rural, suburban and inner city areas. Again some of these are selected at random and subdivided again, e.g. into parliamentary wards and a further random selection made. The process can be repeated until individual households or companies or units of interest are identified.

17. category Multi-stage sampling • Often used in collecting the data from a wide area

18. Sample size • Estimating A Proportion(To calculate a 95% confidence interval for proportion p with margin of error d use a sample of size) Prepresent an expected frequency of the factors under study; q=1-p; d represent an acceptable margin • We usually let d=0.1P as an acceptable margin, then the formula will be • N=400*Q/P

19. Sample size • Epi info • statcalc> sample size & power> population survey> enter p and d

20. Sample size • Estimating a Mean or Mean Difference with Given Precision(with 95% confidence) d represent an acceptable margin, equal to approximately half the confidence interval width S represent standard deviation

21. Content and mode of investigation Content of investigation • Basic content • Disease condition • Factors concerning the study Mode of investigation • Questionnaire (face to face, by telephone, self-administration or even via the mail )

22. Management and Analysis of date • Check • Sorting and grouping • Calculate different rate or mean • Compare standardized rate in three patterns of distribution • Analyze the correlation

23. Limitation of cross-sectional study • Difficult to separate cause from effect.(不能判断因果关系) • Confounding factors may not be equally distributed between the groups being compared and this unequal distribution may lead to bias and subsequent misinterpretation.（不同组别进行比较时，可能有混杂因素） • 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.（现况调查调查的疾病影响因素目前状况，这种状况可能会因为疾病的出现已经发生了改变） • A further limitation of cross-sectional studies may be due to errors in recall of the exposure and possibly outcome.（信息偏倚） • Selection bias because of sampling.

24. Question : • Explain the cross-sectional study design • Understand the process of questionnaire construction • Identify several sampling strategies