Study designs: Cross-sectional studies, ecologic studies (and confidence intervals)

1. Principles of Epidemiology for Public Health (EPID600) Study designs: Cross-sectional studies, ecologic studies (and confidence intervals) Victor J. Schoenbach,PhD home page Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel Hill www.unc.edu/epid600/ Cross-sectional studies

2. Signs from around the world In a Copenhagen airline ticket office: “We take your bags and send them in all directions.”

3. Signs from around the world In a Norwegian cocktail lounge: “Ladies are requested not to have children in the bar.”

4. Signs from around the world Rome laundry: “Ladies, leave your clothes here and spend the afternoon having a good time.”

5. Faster keyboarding - 1 I cdnuolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg. The phaonmneal pweor of the hmuan mnid, aoccdrnig to a rscheearch at Cmabrigde Uinervtisy. It dn'seot mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The rset can be a taotl mses and you can sitll raed it wouthit a porbelm. • Gary C. Ramseyer's First Internet Gallery of Statistics Jokes http://davidmlane.com/hyperstat/humorf.html (#162)

6. Faster keyboarding - 2 Most of my friends could read this with understanding and rather quickly I might add. Then I had them read a statistical bit of literature: • Miittluvraae asilyans sattes an idtenossiy ctuoonr epilsle is the itternoiecsno of a panle pleralal to the xl-yapne and the sruacfe of a btiiarave nmarol dbttiisruein. Gary C. Ramseyer's First Internet Gallery of Statistics Jokes http://davidmlane.com/hyperstat/humorf.html (#162)

7. Principles of Epidemiology for Public Health (EPID600) Study designs: Cross-sectional studies, ecologic studies (and confidence intervals) Victor J. Schoenbach,PhD home page Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel Hill www.unc.edu/epid600/ Cross-sectional studies

8. Today – outline • Cross-sectional studies (and sampling) • Ecologic studies • Confidence intervals Cross-sectional studies

9. Cross-sectional studies • Cross-sectional studies include surveys • People are studied at a “point” in time, without follow-up. • Can combine a cross-sectional study with follow-up to create a cohort study. • Can conduct repeated cross-sectional studies to measure change in a population. Cross-sectional studies

10. Cross-sectional studies • Number of uninsured Americans rises to 50.7 million. (USA Today, 9/17/2010; data from Census Bureau) • In 2007-2008, almost one in five children older than 5 years was obese.(Health, United States, 2010; data from the National Health and Nutrition Examination Survey) • 35% (~7.4 million) of births to U.S. women during the preceding 5 years were mistimed or unwanted (2002 National Survey of Family Growth, Series 23, No. 25, Table 21) [Source: www.cdc.gov/nchs/] Cross-sectional studies

11. Cross-sectional studies • Incidence information is not available from a typical cross-sectional study • Sometimes can reconstruct incidence from historical information • Example: the incidence proportion of quitting smoking, called the “quit ratio”: ex-smokers / ever-smokersis calculated from survey data. Cross-sectional studies

12. Measure prevalence at “point” in time • “Snapshot” of a population, a “still life” • Can measure attitudes, beliefs, behaviors, personal or family history, genetic factors, existing or past health conditions, or anything else that does not require follow-up to assess. • The source of most of what we know about the population Cross-sectional studies

13. Population census • A cross-sectional study of an entire population • Provides the denominator data for many purposes (e.g., estimation of rates, assessing generalizability, projecting from smaller studies) • A huge effort – people can be difficult to find and to count; may not want to provide data • Some countries maintain accurate and current registries of the entire country Cross-sectional studies

14. National surveys conducted by NCHS National Health Interview Survey (NHIS) – household interviews National Health and Nutrition Examination Survey (NHANES) – interviews and physical examinations National Survey of Family Growth (NSFG) – household interviews National Health Care Survey (NHCS) –medical records Cross-sectional studies

15. National surveys • Designed to be representative of the entire country • Modes: household interview, telephone, mail • Employ complex sampling designs to optimize efficiency (tradeoff between information and cost) • Logistically challenging (answering machines, cellphones, . . .) See presentation by Dr. Anjani Chandra at www.minority.unc.edu/institute/2003/materials/slides/Chandra-20030522.ppt Cross-sectional studies

16. Example: National Health Interview Survey • Conducted every year in U.S. by National Center for Health Statistics (CDC) • “Stratified, multistaged, household survey that covers the civilian noninstitutionalized population of the United States” • Redesigned every decade to use new census Cross-sectional studies

17. “multistaged” • Improves logistical feasibility and reduces costs (though reduces precision) 1. Divide population into primary sampling units (PSU’s)PSU = primary sampling unit: metropolitan statistical area, county, group of adjacent counties Cross-sectional studies

18. “multistaged” 2. Select sample of census block groups (SSU’s) within each selected PSU 3. Map each selected census block group or examine building permits 4. Select one cluster of 4-8 housing units dispersed evenly throughout the block NCHS draws a new representative sample for each week’s interviews Cross-sectional studies

19. “stratified” • US divided into 1,900 PSU’s • Largest 52 PSU’s are “self-representing” • Rest of PSU’s divided into 73 categories (“strata”), based on socioeconomic and demographic variables • Sampling takes place separately within each category (“stratum”) Cross-sectional studies

20. Sample size and Precision Cross-sectional studies

21. Weighted sampling Cross-sectional studies

22. “stratified” • Also place census blocks into categories and sample within each • Oversample some strata Cross-sectional studies

23. “Defined population” • Studies, especially cross-sectional studies, are easiest to interpret when they are based in a population that has some existence apart from the study itself (“defined population”) 1. Political subdivision (city, county, state) 2. Institutional (HMO, employer, profession) • Probability sampling enables statistical generalizability to the defined population Cross-sectional studies

24. Surveys of sentinel populations • HIV seroprevalence survey in three county STD clinics in central NC in 1988 • 3,000 anonymous, unlinked, leftover sera • Anonymous questionnaire for demographics and risk factors [Schoenbach VJ, Landis SE, Weber DJ, Mittal M, Koch GG, Levine PH. HIV seroprevalence in sexually transmitted disease clients in a low-prevalence southern state. Ann Epidemiol 1993;3:281-288] Cross-sectional studies

25. HIV seroprevalence [Schoenbach VJ, Landis SE, Weber DJ, Mittal M, Koch GG, Levine PH. HIV seroprevalence in sexually transmitted disease clients in a low-prevalence southern state. Ann Epidemiol 1993;3:281-288] Cross-sectional studies

26. Seroprevalence (% HIV+) by risk factors [Schoenbach VJ, Landis SE, Weber DJ, Mittal M, Koch GG, Levine PH. HIV seroprevalence in sexually transmitted disease clients in a low-prevalence southern state. Ann Epidemiol 1993;3:281-288] Cross-sectional studies

27. Interpretation • Measures prevalence – if incidence is our real interest, prevalence is often not a good surrogate measure • Studies only “survivors” and “stayers” • May be difficult to determine whether a “cause” came before an “effect” (exception: genetic factors) Cross-sectional studies

28. Other points • Can choose by exposure or overall • Can choose by disease – may not be distinguishable from a case-control study with prevalent cases Cross-sectional studies

29. Outline • Cross-sectional studies (and sampling) • Ecologic studies • Confidence intervals Cross-sectional studies

30. “Ecologic” studies • Most study designs – cross-sectional, case-control, cohort, intervention trials – can be carried out with individuals or with groups • Group-level studies which use routinely collected data are easier and less costly • Group-level studies that involve interventions may not be easier or less costly Cross-sectional studies

31. Types of group-level variables • Summary of individual-level variable (e.g., median household income, % with high school diploma) • Property of the aggregate (e.g., neighborhood grocery stores, seat belt legislation, “community competence”) Cross-sectional studies

32. Interpretation • Link between summary exposure variable and individual-level outcome must be inferred • Inference from group to individual is not always sound Cross-sectional studies

33. Example: Male Circumcision and HIV Source: Bongaarts J, et al. The relationship between male circumcision and HIV infection in African populations. AIDS 1989; 3(6): 373-7. (Slope indicates strength of relationship; r indicates linearity) Cross-sectional studies

34. Outline • Cross-sectional studies (and sampling) • Ecologic studies • Confidence intervals Cross-sectional studies

35. Confidence intervals • Provide a plausible range for the quantity being estimated • Width indicates the precision of an estimate for a given level of “confidence” • Confidence intervals quantify only random error from sampling variation, not systematic error from nonresponse, study design, etc. Cross-sectional studies

36. Confidence level vs. precision • The more vague my estimate, the more confident I can be that it includes the population parameter: “I am 100% confident that the prevalence of HIV is between 0 and 100%”. • The more specific my estimate, the lower my confidence: “I am 0% confident that the prevalence of HIV is 5.23%” Cross-sectional studies

37. Confidence intervals – interpretation • Simple interpretations are typically not precise • Precise interpretations are typically not simple Cross-sectional studies

38. Simple but imprecise • “There is 95% confidence that the interval contains the true value” – True, but begs the question – how to define “confidence” Cross-sectional studies

39. Simple but imprecise • “There is a 95% probability that the interval contains the true value”– Not quite correct: probability (as conventionally defined) applies to a process, not to a single instance Cross-sectional studies

40. Probability applies to a process: example A 95% confidence interval can be viewed as a measurement or estimation process that will be correct (the interval includes the true value of the parameter) 95% of the time and incorrect 5% of the time. Let us make up another estimation process that will be correct (about) 95% of the time. Cross-sectional studies

41. Why probability applies to a process • Estimate your gender by flipping a coin 5 times - if the result is 5 heads estimate your gender to be its opposite; otherwise estimate your gender to be what you think it is now. • Probability that estimate will be correct is(1 – Probability of 5 heads) = 0.97 = 97% • Probability that estimate will be incorrect is 3% Cross-sectional studies

42. Why probability applies to a process So we now have a measurement process that will be correct 97% of the time. We will use it to measure your gender. Flip the coin 5 times, and suppose you get 5 heads • Is there a 97% probability that you are of the opposite sex? Cross-sectional studies

43. Precise but not simple A 95% confidence interval is: 1. obtained by using a procedure that will include the population parameter being estimated 95% of the time 2. the set of all population values which are “likely” to yield a sample like the one we obtained Cross-sectional studies

44. Suppose that this line represents the value of the parameter we are trying to estimate True value Cross-sectional studies

45. Possible estimates of that parameter in N identical studies (shows sampling variation) Study estimates True value Cross-sectional studies

46. One possible “true” value and how it would manifest, on average, in N identical studies True value 95% of the distribution Cross-sectional studies

47. Estimate from one study of a given size ? Estimate Cross-sectional studies

48. A possible “true” value with < 2.5% chance of being observed at or beyond the estimate ? Estimate 95% of the distribution Cross-sectional studies

49. A possible true value with > 2.5% probability of being observed at or beyond the estimate ? Estimate 95% of the distribution Cross-sectional studies

50. A possible true value with > 2.5% probability of being observed at or beyond the estimate ? Estimate 95% of the distribution Cross-sectional studies