2. RISK Dr. Kyaw Min MBBS, DTMH, MCTM, MPH, PhD PH FACTM, FRSTMH Lecturer of Tropical Medicine Department of Medicine

3. Lecture Content Definition of risk Estimating association between a population exposed to a factor and risk of developing a disease, compared to a population which was unexposed – ratio and absolute difference Measures of disease association– risk ratio (RR), odds ratio (OR), risk difference (RD), AR, AR%, PAR, PAR%

4. Lecture Objectives 1.u nderstand risk and risk factors 2.explain the meaning of odds ratio, relative risk, and attributable risk 3.apply measures of association between exposure and disease in cross sectional, case control and cohort 4 calculate and interpret the following measures: relative risk, odds ratio, attributable risk, attributable risk %, population attributable risk, population attributable risk % 5. understand usefulness of risk assessment data. By the end of this lecture, students will be able to:

6. Definition of Epidemiology Epidemiology is the study of the distribution and determinants of health- related states and events in populations such as morbidity, injuries, disability and mortality and the application of this study to control health problems. Disease Determinants - risk factors or prior events associated with the appearance of the disease/condition Disease Determinants - risk factors or prior events associated with the appearance of the disease/condition

7. What is “Risk” ? The probability of some untoward event The likelihood that people without the disease (DS - ), but exposed to risk factors (E + ), or who exposed certain clinical or demographic characteristics (risk factors) (E + )  will acquire the disease (DS + ).

8. Key components of epidemiological studies Study Population/ Sample Exposure to a study factor Outcome Unexposed Exposed Target Population

9. What is a Risk Factor ? “A characteristic which, if present and active, clearly increases the probability of a particular disease in a group of persons compared to an otherwise similar group of persons who do not have the characteristic”. Characteristic + Characteristic - ↑ Prob of DS

10. Agent Host Environment Age Sex Genotype Behaviour Nutritional status Health status Infectivity Pathogenicity Virulence Immunogenicity Antigenic stability Survival Weather Housing Geography Occupational setting Air quality Food Factors Influencing Disease Transmission

11. BEINGS Risk Factor (major categories) BEINGS model by Jekel et al 2001 B Biological & behavioural factors E Environmental factors I Immunological factors N Nutritional factors G Genetic factors S Services, Social & Spiritual factors loss of a spouse, culture smoking, inactivity, driving without seat belts, drinking alcohol High-saturated fat diet Radiation following nuclear disaster A high total cholesterol An antibiotic A vaccine

13. Arithmetic Comparisons Measures of association are mathematical comparisons Mathematic comparisons can be done in absolute terms or relative terms simple example: I have RM 2 You have RM 1 "For the things of this world cannot be made known without a knowledge of mathematics."- Roger Bacon

14. Absolute Comparison In absolute terms, I have RM 2 – RM 1 = RM 1 more than you Note: the absolute comparison was made with subtraction It is as simple as that…

15. Relative Comparison Recall that I have RM 2 and you have RM 1. In relative terms, I have RM 2 ÷ RM 1 = 2, or “twice as much as you” Note: relative comparison was made by division

16. Suppose, I am exposed to a risk factor and have a 2% risk of disease. You are not exposed and you have a 1% risk of the disease. Applied to Risks Of course we are assuming we are the same in every way except for this risk factor. In absolute terms, I have 2% – 1% = 1% greater risk of the disease This is the risk difference

17. In relative terms I have 2% ÷ 1% = 2, or twice the risk This is the relative risk associated with the exposure. Applied to Risks

18. Terms Related to Morbidity Incidence of a disease (Incidence rate) The number of new cases of a disease that occur during a specified time period (numerator) in a population at risk for developing the disease (denominator) Prevalence of a disease (Prevalence rate) The number of total cases of disease present at a particular time (numerator) in a specific population (denominator) Risk The likelihood that an individual will contract a disease

19. Concept of the Prevalence “Pool” New cases (Incidence) Death rate Recovery rate RISK is nickname of INCIDENCE

20. Characteristics RISKPREVALENCEINCIDENCE RATE Probability of disease Percent (%) of pop. with the disease Rapidity of disease occurrence No units Cases per person-time Newly diagnosed ExistingNewly diagnosed “Cumulative incidence” (CI) “Incidence density” (ID)

21. Incidence The two forms of incidence are: Cumulative incidence (CI) "risk of disease“ measures the proportion of persons who develop a disease in a known span of time Incidence rate (ID) "rate of disease“ measures the rate of new disease occurrence over time

22. Individual level, is proportion Dimensionless Fixed population Use in selecting treatment, prevention and control, assessing prognosis or severity of disease Range from 0 - 1 Reflect information on population Had dimension Dynamic population Use to test hypothesis for the specific etiology Range from 0 - α CI (Risk) ID (Rate)

23. Risk and Odds Risk (the same as incidence) = Number of new events in specified period (Number of persons exposed to risk during this period) Odds = Number of new events in specified period (Number of persons exposed to risk during this period minus those with events)

24. Probability vs. Odds P = 1/6 = 0.167 or 16.7% Odds = (P) / (1-P) Odds =.167 / (1-.167) =.200 or 20 % 1 1 1 1 1 1 Example: 1 out of 6 patients suffer a stroke……. Probability (P) or “risk” of having an event Odds = ratio of the probability of having an event to the probability of not having the event or P / (1 – P)

25. Risk and Odds – an example 60 out of 100 people have a cold in one year Incidence = 60 ÷ 100 = risk of developing a cold = 0.6 Odds = 60 ÷ 40 = odds of developing a cold =1.5 If disease is rare, odds approximate the risk 1/100 1/99 0.01 0.010101

27. Measures of Effect “estimate of the association between a population exposed to a factor and the risk of developing a disease, compared with a population which was unexposed” To measure, we need to calculate either: the ratio of the measures of disease frequency for the two populations or the absolute difference in disease frequency between the exposed and unexposed -populations cont,,,, 2/1 2-1

28. Measures of Association The measures of effect are: 1.The risk ratio (RR) 2.The odds ratio (OR) 3.Risk difference (RD) [also called attributable risk (AR)] Notation (for subsequent slides) I exp = incidence in the exposed group I 0 = incidence in the unexposed group

29.  a/(a+b)  c/(c+d) D+D – Total E +a ba + b E – c dc + d Totala + cb + da+b+ c+d

30. 1.Risk Difference “ the incidence in the exposed group (I exp ) minus the incidence in the unexposed group (I o ) = I exp - I o Values range from negative infinity to positive infinity Better reflects potential Public Health consequences of exposure, potential impact

31. 2.Risk Ratio “the ratio of the probability (risk) of an event occurring in the exposed to that in the Unexposed population” = I exp ÷ I 0 e.g. Relative Risk, Relative Prevalence Ratio No units used Preferred when attempting to determine cause and effect Used to calculate measures of effect in cohort and interventional studies

32. 3.Odds Ratio “the odds of an event is the probability that an individual experiences the event divided by the probability that they do not” Used to calculate measures of effect in case control studies The odds ratio is the ratio of two odds Calculated by constructing a two by two table

33. Assessing Risk in Epidemiological Studies Case control studies Odds ratio Cohort and Intervention studies Incidence (cumulative, density) Relative risk Attributable risk, AR percent Population attributable risk – PAR, PAR % percent, aetiologic fraction (AF)

34. Cross Sectional Studies Direct assessment of risk is not possible However it can provide estimates of relative risk, (assuming cases and non cases are representative of population with respect to risk factors)

35. 2 x 2 Contingency Table Disease +Disease – Total Exposure + a ba + b Exposure – c dc + d Totala + cb + da+b + c+d

36. Cross Sectional Studies Prevalence rate (exposed) = a / (a+b) Prevalence rate (unexposed)= c / (c+d) Rate ratio (point prevalence ratio)= Rate difference (excess risk among exposed) =

37. Prevalence: An Example Disease +Disease -Total Factor + 20 (a)80 (b)100 (a+b) Factor - 10 ( c)90 (d)100 (c+d) Total30 (a + c)170 (b+ d) Prevalence Ratio = = 2.0 Exposed have twice the prevalence compared to unexposed

38. Case Control Studies Relative risk cannot be calculated directly Odds ratio is used as an estimate of Relative Risk of disease where Cases and controls are representative of the population The risk of the disease is low (Rare disease)

39. Case Control Studies: Odds Ratio (OR) It is a measure of association of exposure and disease It measure of how many times likely are persons exposed to the risk factor to get the disease as compared to those not exposed to the risk factor It is the odds of exposure for cases (with disease) divided by the odds of exposure for controls (without disease)

40. Calculating Odds Ratio Disease + Disease – Total Exposure + a ba + b Exposure – c dc + d Totala + cb + da+b + c+d

41. Odds of Exposure for Cases Probability that a case was exposed = No of exposed cases = Total no of cases Probability that a case was not exposed = No of unexposed cases = Total no of cases The odds of exposure for cases: = Probability cases exposed = = a/c Probability cases unexposed

42. Odds of Exposure for Controls Probability that a control was exposed No. of exposed controls = Total No. of controls Probability that a control was not exposed No. of unexposed controls = Total No. of controls The odds of exposure for controls Probability controls exposed = =b/d Probability controls unexposed

43. Odds Ratio (OR) Odds Ratio =Odds of exposure for cases Odds of exposure for controls =a/c ÷ b/d = =

44. OR< 1OR= 1OR > 1 Odds comparison between cases and control Odds of exposure for cases are less than the odds of exposure for controls Odds of exposure are equal among cases and controls Odds of exposure for cases are greater than the odds of exposure for controls Exposure as a risk factor for the disease Exposure reduces disease risk (Protective factor) Particular exposure is not a risk factor Exposure increases disease risk (Risk factor)

45. Odds Ratio (an example) Stroke (+)Stroke (-) Smoking (+) 100 (a) 350 (b) Non-smiking ( - ) 8 (c ) 108 (d) Total 108 458 Odds Ratio = (a x d) ÷ (b x c) = (100 x 108) ÷ (350 x 8) = 3.86 Those who exposed to smoking were 3.86 times more likely to develop stroke than those who did not smoke.

46. 4 ways of interpretation 1. Smoking increases the odds of having a stroke 2. Smoking reduces the odds of having a non-smoke 3. Not smoking is protective against a stroke 4. Odds of not having a stoke are increased for non-smokers.

47. Risk in Cohort Studies 1.Cumulative incidence (Fixed cohort) 2.Incidence density (Dynamic cohort) 3.Relative risk (RR) 4.Attributable risk (AR) 5.Population attributable risk (PAR) 6.Population attributable fraction

48. 1.Cumulative Incidence (Risk) Cumulative Incidence = CI Total number of new cases of a disease in a given time period Total population at risk at the beginning of same time period Applies to a Fixed Cohort Represents the risks of disease of individual during the period of follow-up Suitable for studies with short follow-up e.g. motor vehicle accidents. CI: indicate Risk or Chance of getting a disease

49. 2.Incidence Density (or) Incidence rate Incidence Density = ID Total number of new cases of a disease in a given time Total person-time of observation at the same time period  Applies to Dynamic Cohort  Follow-up studies in which individuals are at risk for different periods e.g. due to follow-up losses, deaths or entry at different times

50. The calculation of person-time at risk 20002001200220032004 Years at risk 5.0 3.0 5.0 4.0 5.0 1.0 2.5 1.5 5.0 32.0 Total person-year at risk D/S L ID: 3/32 = 0.094 cases/ person-year = 9.4 per 100 person-year = Period in the study

51. Interpretation (ID) This mean that one would expect approximately 9.4% of unaffected individuals per year to develop (----- disease) among a group of individuals similar to those in this study. ID or Rate: indicate the rapidity with which new cases develop over time.

52. 3.Relative Risk (Risk Ratio) How many times more likely are the exposed individuals to become diseased, compared to the non-exposed It does not tell about the magnitude (incidence) It tells the (strength of the association) between the exposure and disease It is a useful measure of effect for studies of disease etiology

53. Relative Risk =Risk of disease in exposed Risk of disease in unexposed =Ie / Io =a /(a + b) ÷ c /(c + d) RR =1: Risk in exposed equals risk in non- exposed (no association) RR >1: Risk in exposed greater than risk in non-exposed (positive association: possibly causal) RR <1: Risk in exposed less than risk in non-exposed (negative association, possibly protective)

54. Relative Risk Cancer (+)Cancer (-) Total Diet at Risk 535 965 1500 Diet not at Risk533516751700 Total 1068 613272000 RR = Persons in risk group is 3.81 times more likely to develop colon cancer than their not at risk counterparts.

55. 4. Attributable Risk (Risk Difference) 1.It provides a measure of what proportion can be attributed to the exposure with the assumption that variables are distributed similarly among exposed and non- exposed group. 2.It is the additional incidence of the disease related to exposure.

56. Attributable Risk (Risk Difference) 3.Since AR represents the actual probability of disease in the exposed, it is a more meaningful expression of risk in most clinical situations.

57. Attributable Risk (Risk Difference) QUESTION: What is the amount of risk that is attributable to the risk factor? = (risk of disease in exposed) minus (risk of the disease in unexposed) =I e – I o = 2-1

58. Attributable Risk (Risk Difference) Interpretation:  AR = 0: Risk in exposed is same as unexposed  AR > 0: Exposure is harmful  AR < 0: Exposure is protective Note: It is useful in public health to eliminate exposure to what is known to cause disease for a large proportion of the population.

59. 5.Attributable Risk % QUESTION: What percentage of the total risk for disease is due to the risk factor ? It is also the % of disease in exposed group that could be eliminated by removing the risk factor.

60. Attributable Risk % Based on Absolute Difference in Risk: AR % (exposed) = Risk (exposed) – Risk (unexposed) x 100 Risk (exposed) = Ie -------- Ie-Io 100------- ?

61. Attributable Risk % Based on Relative Difference in Risk: RR= Ie/Io Io= Ie/RR Substituting into {(Ie-Io)/Ie} x 100 AR % (exposed) = If the risk of disease in the population is small, AR % (exposed) =

62. 6.Population Attributable Risk (PAR) Among the general population, how much of the total risk for a disease is due to the risk factor? It is a measure of the excess incidence of disease in a community that is associated with the occurrence of a risk factor PAR = Risk (total) – Risk (unexposed) This information is useful for deciding which risk factors are particularly important to the overall health of the community.

63. Population Attributable Risk Percent (PAR%) QUESTION: Among the general population what percentage of the total risk for the disease is due to the risk factor ? PAR% = Risk (total) - Risk (unexposed) x100 Risk (total)

64. Uses of Estimating Risk Prediction: modeling disease trends in the population studied Diagnosis: improvement in screening tests Prevention: removal of the risk factor can prevent the disease occurrence Counseling of patients: e.g. smoking and lung cancer (OR, RR) Policy analysis: PAR% in preventive programs

66. Classification of epidemiological study Classification Epidemiological study Observation (Natural exposure) Experiment (Exposure given by researcher) Descriptive (no comparison group) Analytic (with comparison group) Cross-section CohortCase control

67. Descriptive Case report Case series Cross-sectional study

68. Hierarchy of Epidemiological studies Case report Case series Descriptive Cross-sectional study ================================== Cross-sectional study Case-control study Analytic Cohort study ================================== Clinical trial Experimental

69. Cross-sectional study (Descriptive study) D (+)D (-) Total E + a b a+b E -cdc+d Total a+cb+dA+b+c+d Point – prevalence of disease = × k = ---% - prevalence Ratio = (RR in cohort) Prevalence of disease between exposed grp is ( ) times higher than non-exposed

70. Cross-sectional study (Analytic study) Odd Ratio = Measure of association If OR = 1  no association OR > 1  E+ might be a risk factor OR < 1  Protective

71. Cross-sectional study (Analytic study) - prevalence Ratio = (RR in cohort) Interpret as prevalence ratio: * The prevalence of disease in exposed group is (**.**) times higher than in unexposed group. Or * Exposure is (**.**) times more prevalent among people with the disease than those free of disease.

72. Cross-sectional study (Analytic study) Prevalence rate difference = (AR) The difference of prevalence rate of disease among people with exposure (risk factor present) and (risk factor absent) is 0.0♠ or ♠ %. OR The difference of exposure rate among people with the disease and those without disease is 0.0♠ or ♠ %.

73. Cohort study (a group of persons who are followed over time) The most power observational study for identifying an association of risk factors and a disease The most time consuming The most expensive D/s - E+ E- D/S + D/S - D/S +

74. RISK FACTORS Cigarette DISEASE Lung Cancer CASE-CONTROL RISK FACTORS Cigarette DISEASE Lung Cancer COHORT

75. Incidence of smoker who develop Lung CA= 45/500 Incidence of Non-smoker who develop Lung CA= 1/500 RR of smoking for Lung CA= Those who smoked were 45 times more likely to develop Lung CA than those who did not smoke. CA lung (+) No CA (-)Total Smoke (+) 45 (a) 455 (b) 500 Smoke ( - ) 1 (c ) 499 (d)500 Total 46 954 100

76. AdvDisadv: Temporal sequenceInsufficient for the evaluation of rare diseases The effect of Rare ExposureExpensive & time consuming (Pro) Able to examine multiple diseases outcome of a single exposure Adequate records (Retro) Loss to follow-up

77. Concurrent, Retrospective, and Bidirectional Studies

78. Case-control study (point prevalence Rate Ratio) D/S + D/S - E+ E-

79. Rare disease ( Incidence of disease in E+ and E- < 5% per year or Disease Prevalence < 5/1000 ) Cases represent Diseased population and Controls represent Non-diseased population OR ~ RR

80. Optimum for rare disease Optimum for disease with long latency Able to study several exposure Less time and money consuming Usually small sample size Data available Less ethical problem Case-Control Study Advantages

81. Unable to directly compute RR Not suitable for rare exposure Temporal relationship exposure-disease sometimes difficult to establish Prone to bias - Selection bias :- Bias in selection of control, Survivor bias - Information bias :- Recall bias Confounder Sampling error ----  Precision of study Case-Control Study Disadvantages

82. Example In a population of 500,000 Prevalence of smoking is 10 % Incidence of lung cancer among smoker= 150/100,000 Incidence of lung cancer among non-smoker= 15/100,000 Q n: : RR=?, AR=?, AR%, PAR=?, PAR%=?, PAF=?, (with your interpretation)

83. Example In a population of 500,000 Prevalence of smoking is 10 % (P e =0.1) Incidence of lung cancer among smoker= 150/100,000 (I e ) Incidence of lung cancer among non-smoker= 15/100,000 (I o ) RR= (I e )/ (I o )= 10 Interpretation: It is a measure of strength of the association between exposure and disease. Lung CA+ Lung CA - Total Smoking +150100,000 Smoking - 15100,000 Total165200,000

84. RR (a measure of strength of the association between exposure and disease) The RR estimates the magnitude of association between exposure and disease and indicates how much more likely the exposed group is to develop the disease than the unexposed group. RR=1 no association (because of same incidence) RR>1: an increase risk of developing a disease among those exposed to a factor RR<1: a decrease risk of developing a disease among those exposed to a factor (a protective factor)

85. AR= (I e ) - (I o )= 150-15= 135/100,000 (Interp)  Off all lung cancer the number of cases that occurred because of (or attribute to) smoking is 135 per 100,000 population. AR% (Cohort)= (I e - I o )/Ie × 100 AR% (Case-control)= (OR-1)/OR × 100 AR% indicate the proportion of the disease resulting from the exposure in the exposed group AR is talking about exposed group. It is a measure of public health impact of an exposure.

86. PAR= I P – I o I P = Incidence of lung cancer in population = Pe (Ie) + Po (Io) Pe= proportion of exposed group (smokers) in population= 0.1 Po= (1- Pe ) proportion of unexposed group (non-smokers) in population= (1- 0.1)= 0.9 I P = (0.1) (150) + (0.9) (15)= 28.5/100,000 PAR= 28.5-15= 13.5/100,000 (Interp)  If the anti-smoking campaign is successful in terms of elimination of smoking in this population (100% elimination),the magnitude of lung cancer would be reduced by 13.5 per 100, 000 population. PAR is talking whole population

87. PAR (Excess risk in population) PAR is an indicator of the excess risk of disease due to a particular exposure in the total study population, which includes both those exposed and unexposed. Note: PAR is less than AR because I P is a combination of I e and I o. PAR% = (I P – I o )/ I P × (100)= 47% PAR% represents the percentage of a disease in the total population that Could be prevented if the exposure were eliminated

88. Attributable Fraction (AF)= (I e - I o )/ I e AF= 150-15/150= 0.90 or 90 % PAF= (I P – I o )/ I P = (28.5-15)/28.5= 0.47 or 47% (Interp)  In general, based on information on the incidence of disease, 90 % of lung cancer cases could be attributed to smoking (the other 10 % occurred because of other causes) (Interp)  Of all lung cancer in this population, 47% could be attribute to smoking (the other 53% occurred because of other causes) OR If we would eliminate the smoking habit of this population, the magnitude of lung cancer would be reduced by 47%.

89. PAR Answer the question “Among the general population, how much of the total risk for fatal disease is due to exposure?” Value for policy makers

90. PAR% Answer the question “Among the general population, what percentage of the total risk for disease (lung cancer) is due to the exposure?”

91. Uses of Risk assessment data to estimate benefit of a proposed intervention (smoking reduction program) See: page 96 of reference book 1. A smoking reduction program if that can reduce the proportion of adult male smokers from 10% to 5%, eventually would be expected to prevent about * lung cancer deaths per 100,000 men per year.

92. Examines natural history of disease and prognosis of disease

94. References 1. Jekel JF, Katz DL, Elmore JG, Wild DMG: Epidemiology, Biostatistics, and Preventive Medicine, 3 rd Edition, 2007. 2. Gordis L: EPIDEMIOLOGY, fourth edition, 2008.