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The objective of this lecture is to have an idea about some advanced subjects in Epidemiology

This lecture covers advanced subjects in Epidemiology, including the interaction of disease risk factors, statistical methods for agreement between investigators, types of controls in research studies, and measures of prognosis such as case fatality rate, five-year survival, observed survival, median survival time, and relative survival rate.

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The objective of this lecture is to have an idea about some advanced subjects in Epidemiology

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  1. The objective of this lecture is to have an idea about some advanced subjects in Epidemiology

  2. Interaction • When the incidence of a disease in the presence of two or more risk factors differs from the incidence rate expected to result from their individual effect. • Antagonism and synergism :- • Additive • Multiplicative

  3. 1- Additive :- 4 = Usual I.R. of the disease without presence of factor A or B 12 I.R. with presence of factor A 20 I.R. with presence of factor B 12-4 = I.R. because of factor A alone 20-4 = I.R. because of factor B alone Interaction of factors A+B=4+8+16=28 Attribute Risk

  4. 2- Multiplicative :- I.R. in the presence of interaction of factor A & B = 4x3x5x= 60

  5. Agreement An epidemiological statistical method which shows how much two investigators agree in their results excluding chance factors

  6. 1- Percent agreement : ex. Reading x-ray results by two radiologists, the reading will be either: abnormal, suspected, doubtful or normal: Examiner II Abnormal Suspected Doubtful Normal Abnormal A B C D Suspected E F G H Doubtful I J K L Normal M N O P Percent Agreement = X 100 A+F+K+P Total readings Examiner I

  7. 2- In paired observations in which at least one of the findings in each pair was positive, percent agreement = X100 Observer 1 Positive Negative Observer 2 positive a b Negative c d (ignore) a a+b+c

  8. Kappa Statistics : by Cohen 1960 To exclude chance that two unprofessional persons brought from the street and tackle +ve or – ve. The example is CA breast staging by 2 pathologists: Percent observed agreement – percent agreement expected by chance alone Kappa = % observed agreement = X 100 = 91% 100% - percent agreement expected by chance alone 60+31 100

  9. Kappa : (we have to get % agreement expected by chance that is if pathologist I is working by chance, he will diagnose 60% of the specimens as grade II so if he DX. 60 % of all as stage II (even of the second pathologist) so : 60%x65 =39 60%x35 =21 So the new expected results (by chance alone)will be : pathologist A pathologist B Grade II Grade III 100 39 Grade II 26 14 Grade III 21 39 +14 %agreement = = 53’% expected by chance alone,so Kappa = = =80% 100 91% - 53% 38 47 100% - 53%

  10. New Rx. Current Rx. Group 1 Group 2 Group 1 Group 2 Group 2 Group 1 Crossover 1- planned : in randomized trials Randomized

  11. 2- Unplanned: Randomized Surgical Medical surgery No surgery Require surgery Refuse surgery

  12. Compliance • Non compliance • Drop out • Drop in • How to assess Compliance : examination, calculate the # of tablets, tests, regularity of visits,…. • Treatment of non- compliance : education, rewarding, sometime some sort of punishment.

  13. Types Of Controls • Hospital based : • Easily identified • Aware of antecedent exposure • Subjected to the same intangible selection Factors of the cases • But they are ill

  14. 2.Population based: • No selection bias, healthy people, the result could be generalized • Costly, busy people, less motivated, may not recall exposures with the same level of accuracy as the diseased person

  15. 3.Special groups: • Friends, relatives, neighbors… • Healthy, easy, not costly and cooperative…. • Confounding factors related to ethnic background

  16. 4.Single controls: ordinary controls, individual or groups 5.Multiple Control: as in CA, leukemia and x-ray exposure 6. Individual Controls: pre and post

  17. 7. Matched Pairs:

  18. Expressing Prognosis 1.Case fatality rate: usually for short term, acute conditions. For chronic disease and cancer we use:

  19. 2. Five-year survival: The percent of patients who are alive 5 years after treatment begins or 5 years after diagnosis. Nothing magical about the number (5) i.e. it could be 7 or 10 and no biological changes occur during this period to justify it’s use but they see that usually most of deaths from cancer occur within this period so it is used as an index of success in cancer treatment. Two problems: - Lead time bias - 5 years of observation is necessary

  20. 3.Observed survival:

  21. P1= 176 x100 438 P2= 79 x100 176-40 P3= 32 x100 79-23 P4= 14 x100 32-11 P5= 7 x100 14-6

  22. Probability of surviving 1year = P1 Probability of surviving 2year = P1 x P2 Probability of surviving 3year = P1 x P2 x P3 Probability of surviving 4year = P1 x P2 x P3 x P4 Probability of surviving 5year = P1 x P2 x P3 x P4 x P5

  23. 4. Median survival time : • The length of time that half of the study population survives. • Why we use MST? • Less affected by extremes than the mean • If we use the mean we have to wait till all the patients die.

  24. 5. Relative survival rate: Observed survival in people with the disease Expected survival if the disease is absent

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