1. C ROSS - SECTIONAL STUDY Rashmi Gode Moderator-Dr. A. Mehendale 1

2. F RAMEWORK Introduction Definition Design of cross sectional studies Steps of cross sectional studies Biases of cross sectional studies Uses of cross sectional study Advantages and disadvantages Comparison of other 2

3. INTRODUCTION Aims to describe the relationship between Diseases and other factors of interest as they exist in a specified population at a particular time, Conducted on representative samples of a population, either individuals from a group or a set of groups. Cannot distinguish between newly occurring and long established conditions. Measures the frequency (prevalence)of conditions and demonstrate associations. 3

4. Epidemiological Studies Observational Experimental Randomized Controlled Trials Field trial Community trial Experimental Randomized Controlled Trials Field trial Community trial Analytical Cohort Case-control Cross-sectional Ecological Analytical Cohort Case-control Cross-sectional Ecological Descriptive 4

5. CROSS-SECTIONAL STUDIES DIRECTION CROSS-SECTIONAL STUDIES don’t have a direction cohort Cross-sectional Case-control Exposure outcome 5

6. D EFINITION A cross-sectional study is an observational study in which exposure and disease are measured Simultaneously in a given population, It measures the Prevalence of health outcomes or determinants of health, or both,in a population at a point in time or over a short period. 6

7. D ESIGN OF C ROSS - SECTIONAL S TUDIES Defined Population Gather data on exposure and disease Exposure + Outcome + Exposure + Outcome - Exposure - Outcome + Exposure - Outcome - 7

8. T YPES O F CROSS SECTIONAL STUDY 8 Descriptive Yields information about a single variable,(Eg. Hb Contn,capacity to work) OR About each of number of separate variables in a study population OR In a specific population groups Analytical Provides information about the presence and strength of association between variables Permits testing of hypothesis about association

9. S TEPS OF CROSS SECTIONAL STUDIES Questions to askSteps to takeImportant element What is the problem and why should it be studied? CHOOSE THE PROBLEM AND ANALYSE IT Problem identification Prioritizing problem Problem analysis What information is already available? LITERATURE REVIEW Literature and other available information What do we hope to achieve? FORMULATION OF OBJECTIVES General and specific objectives Hypothesis What data do we need to meet out objectives? How will this be collected? RESEARCH METHODOLOGY (cont.) Sampling Variables Data collection techniques Plan for data collection, processing, and analysis Ethics, pilot study 9

10. S TEPS O F CROSS - SECTIONAL S TUDY CONTINUED …. Questions to askSteps to takeImportant element Who will do what and when? WORK PLAN Personnel-training Timetable How will the study be administered? PLAN FOR PROJECT ADMINISTRATION Administration and monitoring What resources do we need? RESOURCE IDENTIFICATION AND ACQUISITION Money Personnel Material, equipment How will we use the results? PROPOSAL SUMMARY, PAPERS AND PRESENTATIO 10

11. How to conduct the cross-sectional study? State your research question SMART -Specific FINER—Feasible Measurable Investigators interest Attainable Novel Realistic Ethical Time bound Relevant Research hypothesis Objectives Background significance of the research question. Define the TOTAL population and the ACTUAL (study) population from which the sample will be drawn. 11

12. Specify your study variables and the ‘scales’ of measurements. Outcome variable: dichotomous, polychotomous, continues, ordinal. Exposure variable Potential confounding factor: make a detailed list of all the variables that can confound the exposure - outcome relationship and specify the scales of their measurement Design a sample 12

13. D ESIGNING A SAMPLE Probability sampling is one in which every member of the population has a known and nonzero probability of being selected into the sample.  Simple random sampling  Systematic sampling  Stratified sampling  Cluster sampling  Multi-stage sampling Non-probability Sampling is a where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. Consecutive Convenience snowball Judgmental(purposive) 13

14. S IMPLE R ANDOM S AMPLING Each member of the population has an equal chance of being selected. Needs a sampling frame (list of all members of the population from which the sample is to be drawn) Sampling frame should be current and accurate. Methods of simple random sampling  Lottery  Table of random numbers  Computer programs 14

15. S YSTEMATIC SAMPLING Is used when elements can be ordered. A selection interval (n) is determined, by dividing the total population listed by the sample size. A random starting point is chosen and every nth person is selected Eg. sample 8houses from a street of 120 houses. Sampling fraction-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 15

16. S TRATIFIED SAMPLING In a stratified sample the sampling frame is divided into non- overlapping groups or strata, e.g. geographical areas, age- groups, genders. A Random sample is taken from each stratum, ( proportional allocation) List of all units with in each stratum required Each stratum is more accurately represented Strata should be homogenous Separate estimates can be obtained for each stratum, and an overall estimate can be obtained for the entire population Each stratum is more accurately represented Strata should be homogenous Separate estimates can be obtained for each stratum, and an overall estimate can be obtained for the entire population 16

17. C LUSTER SAMPLING Used when the population is geographically dispersed or when a sampling frame is not available. Units sampled are clusters of individuals Looses some degree of precision so design effect should be used. Villages Neighborhoods Households Clusters Schools Factories It has larger sampling error 17

18. M ULTISTAGE SAMPLING This method is appropriate when the population is large and widely scattered. The number of stages of sampling is the number of times a sampling procedure is carried out. ƒ The primary sampling unit (PSU) is the sampling unit (or unit of selection in the sampling procedure) in the first sampling stage; The secondary sampling unit (SSU) is the sampling unit in the second sampling stage, etc), E.g.After selection of a sample of clusters (e.g. household), further sampling of individuals may be carried out within each household selected. This constitutes two stage sampling, with the PSU being households and the SSU being individuals. 18

19. N ON PROBABILITY S AMPLING Consecutive  It include all accessible subjects as part of sample Convenience/haphazard/Accidental  The samples are accessible to the researcher.  Subject are easy to recruit Judgmental Or Purposive  Subject with specific goal on focusing on particular interested characteristics of population which answers the research question best. Snowball sampling  Population of interest in study which can be hard to reach or hidden 19

20. E RRORS IN STUDY A) Sampling error( random error) Random error, the opposite of reliability (i.e., Precision or repeatability), consists of random deviations from the true value, which can occur in any direction. Reliability : ( Precision)- This refers to the repeatability of a measure, i.e., the degree of closeness between repeated measurement of the same value B) Non-sampling error (i.e.bias)- the opposite of validity, consists of systematic deviations from the true value, always in the same direction. Validity: This refers to the degree of closeness between a measurement and the true value of what is being measured. Validity addresses the question, how close is the measured value to the true value ? 20

21. Fig showing relationship between the true value and measured values for low and high validity and reliability 21

22. Internal validity: is the degree to which the results of an observation are correct for the particular group of people being studied. External validity or generalizability is the extent to which the results of a study apply to people not in it. Internal validity is necessary for, but does not guarantee, external validity, and is easier to achieve. 22

23. 23 Internal validity Measurement & confounding bias conclusion sample External validity

24. S AMPLE SIZE CALCULATION Estimating a proportion Estimate how big the proportion might be (P) Choose the margin of error you will allow in the estimate of the proportion (say ± w) Choose the level of confidence that the proportion in the whole population is indeed between (p-w) and (p+w). The minimum sample size required, for a very large population (N>10,000) is: n = Z 2 p(1-p) / w 2 24

25. E STIMATING A MEAN The same approach is used but with SE = σ / √n The required (minimum) sample size for a very large population is given by : n = Z 2 σ 2 / w 2 25

26. D ATA COLLECTION AND ANALYSIS Data collection A) Ordinary data- medical records and reporting cards or 1) Advantage: Easy obtaining: easily making dynamic analysis and secular trend ;easily obtain lots of valuable information in short time 2) Disadvantage: poor in reliability B) Temporarily data: to reach a certain aim. Analysis of data Analysis plan Data cleaning Depending on objective of study Make dummy table 26

27. C OLLECTION OF INFORMATION Clinical examinations, special tests & other observations Interviews and questionnaires Clinical records and other documentary sources Disease prevalence study should be in- Two stages. 1. A Screening test 2.More elaborate and specific test. Recommendation for prevalence survey o Should use more than one test method and combine the findings 27

28. A NALYSIS OF ANALYTICAL C ROSS -S ECTIONAL STUDY Objective: Is there any association? If “YES”, then what is the strength of association? Measures Possible Is there any association? Chi-square, student-t test, etc What is the strength of association? Odds ratio, Rate ratio, Rate difference, Difference between mean, Correlation, Regression coefficient. Measure of impact Risk factor Attributable fraction (exposed) Attributable fraction (population) Protective factor Prevented fraction (exposed) Prevented fraction (population) 28

29. A NALYSIS OF DESCRIPTIVE C ROSS - SECTIONAL STUDY 29 Objective: To describe the disease in time, place and person To generate hypothesis Measures possible Means & SD Median & percentile Proportions – Prevalence Ratios Age, sex or other group specific analysis

30. PREVALENCE Proportion of a defined population that has health outcome uses existing cases of the health outcome  Cases whose health outcome developed or was diagnosed before they were identified for the study Quantifies the burden on population Useful for planning health services Prevalence - # of people with the health outcome # of people in the total population E.g.“ Malaria is a life- threatening disease caused by parasite that are transmitted to people through the bites of infected mosquitos 30 Malaria infects 10% of the world’s population

31. Point Prevalence Assessing disease / attribute frequency at a point of time Period prevalence- during a defined period Number of individuals with disease at a point in time Point Prevalence = x k Number of individuals studied at that time Number of individuals with disease at a point in time Point Prevalence = x k Number of individuals studied at that time Number of individuals with disease in the stated time period Period Prevalence = x k Population at risk Number of individuals with disease in the stated time period Period Prevalence = x k Population at risk 31

32. Life time prevalence- A period prevalence referring to the whole of the subject’s prior life 32 Number of individuals with evidence of disorder(Pastor Present) Life time Prevalence = x k Number of individuals studied Number of individuals with evidence of disorder(Pastor Present) Life time Prevalence = x k Number of individuals studied

33. P REVALENCE V S. I NCIDENCE 33 Prevalent cases All cases ( of disease) Individuals with outcome if interest,regardless of when diagnosed Calculating prevalence Incident cases NEW cases ( of disease) Individuals who change in status over a specified period of time Calculating risks,rates Prevalence = Incidence x mean duration of disease

34. M EASURES OF A SSOCIATIONS : O DDS R ATIO Exposure Outcome Yes No Total presenta=10b=90a+b=100 AbsentC=50D=850c+d=900 Totala+c=60b+d=940N=100 34 OR = (a/c) / (b/d)= ad/bc (10/50)/(90/850)=0.20/10.58= 0.0189 OR = (a/c) / (b/d)= ad/bc (10/50)/(90/850)=0.20/10.58= 0.0189 o OR- is the ratio of one odds to another. o It is the probability that something is so or will occur to the probability that is not so or will not occur.

35. OR 35 Odds of disease among exposed Disease OR = ------------------------------------- Odds of disease among not exposed Odds of exposure among diseased Exposure OR = ------------------------------------- Odds of exposure among not diseased

36. 36 Rate ratio Prevalence ratio = {a/(a+b)}/{c/(c+d)} = 1.8 Exposure ratio = {a/(a+c)}/{b/(b+d)} = 1.74 Rate differences Prevalence difference = {a/(a+b)} - {c/(c+d)} = 0.0444 Exposure difference = {a/(a+c)} - {b/(b+d)} = 0.07 Number needed to avoid one case in unexposed group = 1/prevalence difference = 1/0.0444=22.5

37. M EASURES OF IMPACT If the factor is a risk factor Excess risk among exposed: Prevalence difference-a/(a+b) – c/(c+d) Population excess risk = (a+c)/n – c/(c+d) = Attributable fraction (exposed): [a/(a+b) – c/(c+d)] / [a/(a+b)] x 100 Attributable fraction (population): [(a+c)/n – c/(c+d)] / [a+c)/n] x 100 37

38. M EASURES OF IMPACT If the factor is a protective factor Excess risk among unexposed: c/(c+d) - a/(a+b) Population excess risk = (a+c)/n – a(a+b) Prevented fraction (exposed): [c/(c+d) - a/(a+b)] / [c/(c+d)] x 100 Prevented fraction (population): [c/(c+d) - (a+c)/n] / [c/(c+d)] x 100 38

39. W HICH MEASURE TO USE ? Causal relationships ORs o Magnitude of a public Differences health problem between prevalence 39

40. N ON - RANDOM ERROR - BIAS Selection bias- occurs when comparison are made between group of patient that differ in determinant of outcome. Eg Sample bias Non response bias Non participation bias Berkson’s bias Measurement bias -occurs when methods of measurement /classification of subjects are dissimilar among groups  Interviewers bias  Recall bias  Response bias Confounding bias: Confounding occurs when the effects of two exposures (risk factors) have not been separated and the analysis concludes that the effect is due to one variable rather than the other 40

41. C ONFOUNDING : E G. RELATIONSHIP BETWEEN COFFEE DRINKING ( EXPOSURE ), HEART DISEASE ( OUTCOME ), AND THIRD VARIABLE ( TOBACCO USE ) 41

42. G UIDELINE FOR CRITICAL APPRAISAL OF PREVALENCE STUDY 1.Are the study design & sampling method appropriate for the RQ? 2.Is the sampling frame appropriate? 3.Is the sample size adequate? 4. Are objective, suitable and standard criteria used to measure the health outcome? 5.Is the health outcome measured in unbiased manner? 6.Is the response rate adequate? Are the refusers described? 7.Are the estimates of prevalence given with CI & in detail by subgroup – if appropriate? 8.Are the study subjects and the setting described in detail ?and similar to those of interest to you? 42

43. U SES OF CROSS - SECTIONAL S TUDIES Community diagnosis Surveillance Community education and community involvement Evaluation of community’s health care Individual care Family care Community oriented primary care Community Health Care Clinical Practice 43 o provide new knowledge -(studies on etiology, growth & development)

44. Relatively inexpensive and takes up little time to conduct; Estimate prevalence of outcome of interest because sample is usually taken from the whole population; Many outcomes and risk factors can be assessed; There is no loss to follow- up. Difficult to make causal inference; Only a snapshot: the situation may provide differing results if another timeframe had been chosen; Prevalence-incidence bias (also called Neyman bias)..) Not suitable for rare disease AdvantagesDisadvantages 44

45. 45

46. A DVANTAGES AND DISADVANTAGES OF DIFFERENT OBSERVATIONAL STUDY DESIGN Ecological study Cross sectional Case control cohort Probability of Selection biasNAmediumHighlow Recall biasNAhigh low Loss to follow up NA lowhigh confoundingHIGHmedium low Time requiredLOWmedium high 46

47. C OMPARISON OF DIFFERENT STUDY DESIGN 47 Cross sectional

48. REFERENCES Gordis L.Epidemioligy,5 th edition,2013,Elsever’spublication Abramson J H.Oxford Textbook of Public health, 6.3 Section 510-523,Oxford university press. WHO/CDS/2002 Part I,Introduction to basic epidemiology and principles of statistics for tropical diseases control,Learners Guide R Bonita,R Beaglehole,Basic Epidemiology. World Health Organisation, Geneva: 2 nd Edition,2006 Springer Verlag, et al. Doing Your Research Project: A Guide for First-Time Researchers in Education and Social Science. SpringerLink Springer R&D online Train [Internet]. 2013;13 Volumes(Health services and outcomes research methodology (Online), HSORM):368. Janer L peacock,Oxford handbook of Medical statistics Oxford text book of Epidemiology. 48