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Cohort, case-control & cross- sectional studies Source: Alain Moren, EPIET Introductory courses Kostas Danis EPIET Introductory course Menorca, Spain 16/9-12/10/2012.

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Presentation on theme: "Cohort, case-control & cross- sectional studies Source: Alain Moren, EPIET Introductory courses Kostas Danis EPIET Introductory course Menorca, Spain 16/9-12/10/2012."— Presentation transcript:

1 Cohort, case-control & cross- sectional studies Source: Alain Moren, EPIET Introductory courses Kostas Danis EPIET Introductory course Menorca, Spain 16/9-12/10/2012

2 Epidemiological studies Two types Observation Experiment

3 Exposed Not exposed Disease occurrence Unethical to perform experiments on people if exposure is harmful Exposure assigned

4 Randomised Controlled Trial Blinded Doses Time period Risk - effect No bias If exposure not harmful Treatment Preventive measure (vaccination) If RCT not possible Left with observation of experiments designed by Nature Cohort studies Cross-sectional studies Case control studies

5 Cohort studies marching towards outcomes

6 What is a cohort? One of 10 divisions of a Roman legion Group of individuals - sharing same experience - followed up for specified period of time Examples - EPIET cohort 2012 - birth cohort - cohort of guests at barbecue - occupational cohort of chemical plant workers - influenza vaccinated in 2011-12

7 follow-up period

8 Calculate measure of frequency Cumulative incidence - incidence proportion - attack rate (outbreak) Incidence rate end of follow-up

9 Cohort studies Purpose - Study if an exposure is associated with outcome(s) - Estimate risk of outcome in exposed and unexposed cohorts - Compare risk of outcome in two cohorts Cohort membership - Being at risk of outcome(s) studied - Being alive and - Being free of outcome at start of follow-up

10 unexposed exposed Cohort studies

11 unexposed exposed Incidence among exposed Incidence among unexposed Cohort studies

12 ate ham did not eat ham illnot ill Total a b a+b c d c+d Presentation of cohort data: 2x2 table Risk in exposed= a/a+b Risk in unexposed= c/c+d

13 Incidence rate Number of NEW cases of disease Total person - time of observation

14 Incidence rate Number of NEW cases of disease Total person - time of observation Rate Denominator: - is a measure of time - the sum of each individuals time at risk and free from disease

15 ABCDEABCDE 90 91 92 93 94 95 96 97 98 99 Time at risk x x 6.0 10.0 8.5 5.0 Total years at risk 35.5 -- time followed x disease onset Person-time

16 ABCDEABCDE 90 91 92 93 94 95 96 97 98 99 00 Time at risk x x 6.0 10.0 8.5 5.0 Total years at risk 35.5 -- time followed x disease onset Incidence rate (IR) (Incidence density) IR = 2 / 35.5 person years = 0.056 cases / person year = 5.6 cases / 100 person years = 56 cases / 1000 person years

17 Person-yearsCases Smoke 102,600 133 Do not smoke 42,800 3 Presentation of cohort data: Person-years at risk Tobacco smoking and lung cancer, England & Wales, 1951 Source: Doll & Hill

18 Presentation of data: Various exposure levels

19 time ExposureStudy starts Disease occurrence Prospective cohort study time ExposureStudy starts Disease occurrence

20 Retrospective cohort study Exposure time Disease occurrence Study starts

21 Recipe: Cohort study Identify group of - exposed subjects - unexposed subjects Measure incidence of disease Compare incidence between exposed and unexposed group

22 unexposed exposed Incidence among exposed Incidence among unexposed Cohort studies

23 Absolute measures - Risk difference (RD) I e - I ue Relative measures - Relative risk (RR) Rate ratio Risk ratio Effect measures in cohort studies I e I ue I e = incidence in exposed I ue = incidence in unexposed

24 Exposed Not exposed Cases Non cases Risk % Cohort study 50 50 50 % 10 90 10 % Risk ratio 50% / 10% = 5 Total 100

25 ate ham did not eat ham illnot ill Incidence 49 49 98 50 % 4 6 10 40 % Risk difference 50% - 40% = 10% Relative risk 50% / 40% = 1.25

26 Interpretation of Risk Ratios RR>1 RR=1 RR<1 Protective factor Risk factor No association

27 Does HIV infection increase risk of developing TB among drug users?

28 Vaccine efficacy (VE) VE = 1 - RR = 1 - 0.28 = 72%

29 Population Cases Incidence a1a1 High N1N1 I 1 c Unexposed N ne I ue at risk Exposure level a2a2 Medium N2N2 I 2 a3a3 Low N3N3 I 3 Various exposure levels

30 Population Cases Incidence RR a1a1 High N1N1 I 1 c Unexposed N ne I ue at risk Exposure level a2a2 Medium N2N2 I 2 a3a3 Low N3N3 I 3 RR 1 RR 2 RR 3 Reference Various exposure levels

31 Cohort study: Tobacco smoking and lung cancer, England & Wales, 1951 Source: Doll & Hill

32 Disadvantages of cohort studies Large sample size Latency period Cost Time-consuming Loss to follow-up Exposure can change Multiple exposure = difficult Ethical considerations

33 Strengths of cohort studies Can directly measure - incidence in exposed and unexposed groups - true relative risk Well suited for rare exposure Temporal relationship exposure-disease is clear Less subject to selection biases - outcome not known (prospective)

34 Can examine multiple effects for a single exposure PopulationOutcome 1Outcome 2Outcome 3 exposedN e I e1 I e2 I e3 unexposedN ne I ue1 I ue2 I ue3 RR 1 RR 2 RR 3 Cohort studies

35 A cohort study allows to calculate indicators which have a clear, precise meaning. The results are immediately understandable.

36

37 Cross-sectional (prevalence) studies

38 Cross-sectional studies Observation of a cross-section of a population at a single point in time Recruitment of study participants Population Population sample Observation for the presence of: One or more outcomes One or more exposures

39 Sampling Sample Target Population Sampling Population

40 Uses of cross-sectional surveys in public health Estimate prevalence of disease or their risk factors Estimate burden Measure health status in a defined population Plan health care services delivery Set priorities for disease control Generate hypotheses Examine evolving trends - Before / after surveys - Iterative cross-sectional surveys

41 Potential objectives of a cross sectional study Descriptive Estimate prevalence Analytic Compare the prevalence of a disease in various subgroups, exposed and unexposed Compare the prevalence of an exposure in various subgroups, affected and unaffected

42 IllNon illTotal Exposedab a+b Non exposedcd c+d Presentation of the data of an analytical cross sectional study in a 2 x 2 table Simultaneous measurement of outcomes and exposures

43 Exposed Not exposed Cases Non cases Prevalence % Cross-sectional study 500 500 50 % 100 900 10 % Prevalence ratio (PR) 50% / 10% = 5 Total 1,000

44 Measuring association in analytical cross-sectional surveys Prevalence among exposed / prevalence among unexposed Prevalence ratio Formula equivalent to risk ratio Concept different - No incidence - Only prevalence depends on both occurrence of new cases & duration of disease

45 Prevalence of West Nile virus (WNV) infection by place of residence, Central Macedonia,Greece, 2010 InfectedTotal Prevalence ratio Rural384917.7%5.9 Urban32321.3%Ref

46 Prevalence of HIV infection by socioeconomic status, African country X, 1999 InfectedTotal Prevalence ratio High class 152356.4%2.6 Low class 114502.4%Ref

47 Prevalence of hepatitis C (HCV) infection by quantity of therapeutic injections, Hazabad, Pakistan, 1993 No.of injection s InfectedTotal Prevalence ratio >1094122%22 0-104528%8 01821%Ref

48 Advantages of cross-sectional surveys Fairly quick Easy to perform Less expensive Adapted to chronic diseases

49 Limitations of cross-sectional surveys Limited capacity to document causality (exposure and outcome measured at the same time -difficult to establish time sequence of events) Not useful to study disease etiology Not suitable for the study of rare / short diseases Not adapted to severe / acute diseases Not adapted to incidence measurement.

50 Limitations of causal inference in analytical cross sectional studies Prevalent cases Exposure and outcome examined at the same time

51

52 Principle of case control studies

53 Our objective is to compare the incidence rate in the exposed population to the rate that would have been observed in the same population, at the same time if it had not been exposed

54 Exposed Unexposed Source population

55 Cases Exposed Unexposed Source population

56 Cases Exposed Unexposed Source population Sample Controls

57 Cases Exposed Unexposed Source population Controls: Sample of the denominator Representative with regard to exposure Controls Sample

58 Intuitively if the frequency of exposure is higher among cases than controls then the incidence rate will probably be higher among exposed than non-exposed

59 Case control study Disease Controls Exposure ???? Retrospective nature

60 CasesControls Exposeda b Not exposedc d Totala + c b + d % exposed Distribution of cases and controls according to exposure in a case control study

61 CasesControls Exposeda b Not exposedc d Totala + c b + d % exposeda/(a+c) b/(b+d) Distribution of cases and controls according to exposure in a case control study

62 Oral Myocardial contraceptivesInfarction Controls Yes 693 320 No 307 680 Total10001000 % exposed69.3% 32% Distribution of myocardial infarction by oral contraceptive use in cases and controls

63 PhysicalMyocardial activityInfarction Controls >= 2500 Kcal190230 < 2500 Kcal176136 Total366366 % exposed51.9%62.8% Distribution of myocardial infarction by amount of physical activity in cases and controls

64 WaterCases Controls Consumption YES 150 ? NO 50 ? Total 200 200 Volvo factory, Sweden, 3000 employees, Cohort study 200 cases of gastroenteritis

65 Two types of case control studies Exploratory New disease New risk factors Several exposures "Fishing expedition" Analytical Define a single hypothesis Dose response

66 Cohort studies Rate/risk Rate/risk difference Rate Ratio/Risk ratio (strength of association) No calculation of rates/risks Proportion of exposure Case control studies Any way of estimating measures of association?

67 Odds Probability that an event will happen Probability that an event will not happen Probability that cases/controls will be exposed Probability that cases/controls will not be exposed

68 Exposed Not exposed Cases Controls Odds ratio Case control study a b c d Total a+c OR= (a/c) / (b/d) = ad / bc a/c b/d Odds of exposure b+d % exposed a/(a+c) % unexposed c/(a+c) b/(b+d) d/(b+d)

69 Exposed Not exposed Cases Controls Odds ratio Case control study 50 20 4 a b 50 80 c d Total 100 OR= (a/c) / (b/d) = ad / bc = (50x80) / (20x50) = 4 50/5020/80 Odds of exposure

70 Case control study design CasesControls E E a b c d ab a x d ---- ---=--- ---- cdb x c Odds ratio

71 Frequency of chicken consumption in campylobacter cases and controls, Republic of Ireland and Northern Ireland, 2003 CasesControls Odds ratio Ate chicken1812512.1 Did not eat chicken 1544Ref

72 Frequency of contact with a dog in campylobacter cases and controls, Republic of Ireland and Northern Ireland, 2003 CasesControls Odds ratio Contact with dog Yes29930.40 No158201Ref

73 Oral Myocardial contraceptivesInfarctionControlsOR Yes 693 3204.8 No 307 680Ref. Total10001000 Odds 693/307= 320/680= of exposure 2.2 0.5 Distribution of myocardial infarction by recent oral contraceptive use in cases and controls

74 PhysicalMyocardial activityInfarctionControlsOR >= 2500 Kcal1902300.64 < 2500 Kcal176136Ref. Total366366 odds of 190/176= 230/136= exposure 1.1 1.7 Distribution of myocardial infarction by amount of physical activity in cases and controls

75 Distribution of cases of endometrial cancer by oestrogen use in cases and controls Oestrogen useCasesControlsOdds ratio High a1b1a1d/b1c Low a2b2a2d/b2c None cdReference

76 Relation of hepatocellular adenoma to duration of oral contraceptive use in 79 cases and 220 controls Months of OC useCasesControlsOdds ratio 0-12 7121Ref. 13-3611 49 3.9 37-6020 2315.0 61-8421 2018.1 >= 8520 749.7 Total79220 Source: Rooks et al. 1979

77 Advantages of case control studies Rare diseases Several exposures Long latency Rapidity Low cost Small sample size Available data No ethical problem

78 Limitations of case-control studies Cannot compute directly risk Not suitable for rare exposure Temporal relationship exposure-disease difficult to establish Biases +++ - control selection - recall biases when collecting data Loss of precision due to sampling

79 The cohort study is the gold-standard of analytical epidemiology CASE-CONTROL STUDIES HAVE THEIR PLACE IN EPIDEMIOLOGY, but if cohort study possible, do not settle for second best

80 Thank you!

81 Back-up slides

82 I 1 = a / P 1 I 0 = c /P 0 E E a c P1P1 P0P0 Population denominator Cases E E a c P 1 /10 P 0 /10 Population sample Cases a/P 1 I 1 / I 0 = ------ c/P 0 } a I 1 = -------- P 1 / 10 c I 0 = -------- P 0 /10 } a/P 1 I 1 / I 0 = ------ c/P 0

83 I 1 = a / P 1 I 0 = c /P 0 CasesControls E E a b cd E E a c P1P1 P0P0 Source population Pop.Cases P 1 b --- = ---- P 0 d = sample a/P 1 I 1 / I 0 = ------ c/P 0 }

84 I 1 = a / P 1 I 0 = c /P 0 Cases = sample E E a b cd Since d/b = P 0 / P 1 E E a c P1P1 P0P0 Source population Pop.Cases a/P 1 a. P 0 a. d I 1 / I 0 = ------ = ------- = ----- = c/P 0 c. P 1 c. b } Controls P 1 b --- = ---- P 0 d a / c ------ b / d


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