Cross Sectional Studies Son Hee Jung 2013/03/25

1. Cross Sectional Studies Son HeeJung 2013/03/25

2. Type of Epidemiological Studies Type of study Alternative name Unit Experimental RCT clinical trial individuals Observational Ecological correlational population Cross sectional prevalence individuals Case-control case-reference individuals Cohort follow up individuals

3. Study Designs & Corresponding Questions • Cross-sectional How common is this disease or condition? • Ecologic What explains differences between groups? • Case-control What factors are associated with having a disease? • Prospective How many people will get the disease? What factors predict development?

4. Contents • Definition • Basic approach • Advantage & disadvantage • Sampling • Measures of disease • Prevalence • Bias

5. Cross-sectional study-definition

6. Cross Sectional Study 연구대상 집단 요인 노출과 질환에 관한 정보 수집 한 시점 연구 진행

7. Cross-sectional study- Characteristics

8. Basic approach • Include a sample of all persons in a population at a given time without regard to exposure or disease status • Typically exposure and diseases assessed at that one time • Exposure subpopulations can be compared with respect to disease prevalence

9. Basic approach • For some questions, temporal ordering between exposure and disease is clear and cross sectional studies can test hypothesis • Example: genotype, blood type • When temporal ordering is not clear can be used to examine relations between exposure and outcomes descriptively, and to generate hypotheses • Can combine a cross sectional study with follow up to create a cohort study

10. Basic approach • Issues with addressing etiology • Temporal ordering between exposure and outcome cannot be assured • Length biased sampling • Cases with long duration will be over represented

11. Cross -Sectional Studies: Advantages • Inexpensive for common diseases • Should be able to get a better response rate than other study designs • Relatively short study duration • Can be addressed to specific populations of interest

12. Cross-Sectional Studies : Disadvantages • Unsuitable for rare or short duration diseases • High refusal rate may make accurate prevalence estimates impossible • More expensive and time consuming than case-control studies • No data on temporal relationship between risk factors and disease development

13. Why sample?

14. Sampling from the source population

15. Non-probability sampling • Common convenience sampling methods • Street surveys • Use convenient place such as mall, hospital • Mail-out questionnaires • Most dangerous • Feel very strongly about the issue->bias • Volunteer call • Selection bias

16. Non-probability sampling-Convenience sampling • Select a sample through an easy, simple or inexpensive method • Problem • High risk of creating a bias • May provide misleading information • Can be accepted, but… • Be careful in assessing • And the results they produce

17. Basic probability sampling • Simple random sampling • Each sample of the chosen size has the same probability of being selected

18. Basic probability sampling • Systematic sampling • Obtain a lost of an available population, ordered according to an unrelated factor • Pick a number n as step size • Pick every n-th subject of the list

19. Stratifiedrandom sampling

20. Clusterrandom sampling

21. Multistage sampling

22. The National Health and Nutrition Examination Survey (NHANES)

23. NHANES Interviews & Examinations • ㅍ

24. NHANES Sample Design

25. Analyses of NHANES Data

26. Weighting in NHANES • ㅍ

27. NHANES base probability of selection • ㅍ

28. Oversampling

29. Sample Weights

30. Why weight?

31. Probability weight – simple example

32. Example of weighting • Imagine 100 male & 100 female in sample • But only 80 males & 75 females respond • Male respondent will get weight of • 100/80->1/(80/100)=1.25 • Female respondent will get weight of • 100/75->1/(75/100)=1.33

33. 국민건강영양조사의 표본추출방법 예

34. 다단계 표본추출 • 단순무작위 표본추출의 실제적 어려움을 해결하기 위해 고안된 방법 • 전국 규모의 여론조사에 이용 • “series” of simple random samples in stages • 국민건강영양조사 random sampling random sampling random sampling

35. 유병률 산출: 가중치 적용 • 목적: 국민건강영양조사의 표본이 우리나라 국민을 대표하도록 가중치를 사용

36. Directage adjustment-before

37. Directage adjustment-after Age-adjusted rates: 2238/1800000=124.3 1830/1800000=101.7

38. Indirect age adjustment (Standardized Mortality Ratio) • When • number of deaths for each age-specific strata are not available • Study mortality in an occupational exposure population • Defined Observed number of deaths per year Expected number of deaths per year • SMR of 100 • Observed number of deaths is the same as expected number of deaths SMR= X100

39. Sampling, Inference, and generalization

40. Sampling, Inference, and generalization

41. Sampling, Inference, and generalization If you tell the truth you don't have to remember anything. by Mark Twain 1894

42. Why do we measure disease prevalence?

43. Measuring burden: prevalence

44. Prevalence

45. Measuring burden: prevalence

46. Person-time at risk: exposed and unexposed

47. Censored individuals

48. Censoring

49. Measuring of prevalence

50. Point and period prevalence: example