Cross-Sectional Studies

1. Cross-Sectional Studies NargesKhanjani, MD, PhD, Fellowship in Environ Epi

3. 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

4. Definition • Cross-sectional studies are studies of prevalence. Proportion with an attribute or disease / Number of subjects = Prevalence. • a type of observational or descriptive study • the research has no control over the exposure of interest (e.q. diet). • 3 important questions to consider: • Definition of Case • Definition of the Population • Are cases and non-cases from an unbiased sample of the population?

5. Definition • “Snapshot Studies” (Paffenbarger, 1988) • Observations at a single hypothetical point in time • Each subject assessed once at point in time. • Point Prevalence Studies

6. Definition • also called a Prevalence survey • A study that is quick and inexpensive to complete. • Designed to determine “ what is happening ? right now”

7. Basic features • “Snapshot” of a population, a “still life” • Assesses both the exposure and outcome simultaneously, at a single point in time • Calculates prevalence, but not incidence • A study that is quick and inexpensive to complete. • The first step in testing associations

8. Uses • Prevalence used in planning • Individual: Pre-treament probability for Dx • Population: Health care services • Examine associations among variables • Hypothesis generating for causal links

9. Uses • Identify and describe a problem • Collect information for planning e.g. surveys of immunisation, antenatal care, coverage • Evaluate utilisation rates of services • Monitoring health status of a community by regular repeated surveys

10. Uses • Hypothesis generating for causal links • Method of Difference: If frequency of a disease is markedly different between two groups then it is likely to be caused by a particular factor that differs between them. • Method of Agreement: If a factor commonly occurs in which a disease occurs with high frequency then the factor is very likely associated with the disease. • Concomitant variation: Frequency of a factor varies in proportion to frequency of disease.

11. Uses • 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?) • The British National Diet and Nutrition Survey • 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.

13. 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.

15. 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.

16. 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.

17. 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.

20. Measure: Prevalence • Measure exposure and outcome variables at one point in time. • Main outcome measure is prevalence P = Number of people with disease x at time t Number of people at risk for disease x at time t Prevalence=k x Incidence x Duration

21. Measure: Prevalence Example: RQ: What is the prevalence of chronic pain after hernia surgery? Exposure of interest: Hernia surgery Outcome of interest: Chronic pain (lasting for more than 3 months) Methods: questionnaire survey Sample: All patients who had a hernia procedure between 1995-1997 n=350 Results: Period prevalence chronic pain = 30% (CI 95% 24 - 36%) Point prevalence chronic pain = 25% (on day of survey)

22. 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)

23. Study Design Disease (Outcome) _ + + Exposure (Risk Factor) _

24. Things to consider when designing a cross-sectional study (survey) • What is your research question? • Is the design appropriate for your study? • Who are you going to study? • How are you going to obtain your sample? • Everyone who is eligible should have an equal chance of being invited to take part • Is there a risk of ‘selection bias’? • E.g. taking people attending a specialist clinic; might not be ‘representative’ of all patients with that condition • Selection bias is a threat • How you will collect your exposure/outcome data • Think about analysis (proportion %, denominator)

25. Things to consider when designing a cross-sectional study (survey) • In Cross-sectional studies think of: • Sampling Procedures. • Clear definition of Target Population. • Clear definition of outcome. • Clear definition of risk factors. • Remember Confounders.

26. Sampling Sampling • A sample is a subset of the population • Can be random or non-random; can be representative or non-representative • Different types of sampling • This is major challenge when doing cross-sectional studies

27. Methods for collecting data • face to face interview • mail questionnaire • telephone interview • Self-administrated questionnaire • Medical examination • Laboratory test

28. Issues in collecting data • To sure what data shall be obtained • To sure which index will be used • Methods for collecting data • Criteria of disease diagnosis • Definition of variables • Training investigators

29. Variable assessment in cross-sectional studies • 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

30. 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).

31. Analysis • 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.

32. Analysis • Descriptive analyses • Analysis of differences • Analysis of association / relationship • Multivariable analysis

33. Analysis • 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.

34. Analysis DZ = Rash (+)(–) 95 183 88 Grape 8 35 43 Tomato Prevalence ratio = 52%/19% = 2.6

35. Analysis • Instead of looking at a ratio of prevalences, we can also look at a ratio of odds. • Odds are not intuitively appealing: they are the likelihood of an event occurring divided by the likelihood of the event not occurring.

36. 95 • 88 • 8 • 35 Analysis DZ = Rash (+)- Grape 95/183 PR= ------- =2.6 8/43 183 43 Tomato Odds of grape work in rash pts: 95/8=11.9 Odds of grape work in healthy: 88/35=2.5 Odds ratio=(95/8)/(88/35)=11.9/2.5=4.7

37. Bias Selection Bias Is study population representative of target population? Measurement Bias Outcome • Misclassified (dead, misdiagnosed, undiagnosed) • Length-biased sampling • Cases overrepresented if illness has long duration and are underrepresented if short duration.(Prev = k x I x duration) Risk Factor • Recall bias • Prevalence-incidence bias • RF affects disease duration not incidence

38. Bias • The selection bias classic for cross-sectional studies is “the healthy worker effect.” I.e., only “healthy workers” are available for study, distorting your findings. • Example: Low asthma rates in animal handlers (because persons contracting asthma quit and are not available for study).

39. Quick, cheap Easy to obtain prevalence Outcome Exposure Advantages

40. Prone to selection bias Recall bias Cannot measure disease onset Problem of temporality (not a problem if exposure is constant) Not suitable for rare disease Disadvantages

41. Limitation of cross-sectional study • It is not possible to say exposure or disease/outcome is cause and which effect • 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 reporting of the exposure and possibly outcome.