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BASIC CONCEPTS IN EPIDEMIOLOGY Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University.

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Presentation on theme: "BASIC CONCEPTS IN EPIDEMIOLOGY Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University."— Presentation transcript:

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2 BASIC CONCEPTS IN EPIDEMIOLOGY Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University

3 WHAT IS EPIDEMIOLOGY From Greek language Epi…………………On, Upon, Among Demos…………….The people Logos……………...Theory, Study Epidemiology is the study of disease occurrence in human population

4 The only medical subspecialty that is concerned with the occurrence of illness over time WHAT IS EPIDEMIOLOGY TIME 1 Disease absent Disease present or absent TIME 2

5 Fundamental Assumptions Human disease Does not occur at random Has causal and preventive factors Is a consequence of specific exposures Environmental, Biological Behavioral Radiation…………………….…………..Cancer Reduced fluoride…………..……Dental carries Second hand smoke…….Respiratory disease Viruses………………………………....Measles Bacteria………………………...…..Pneumonia Cigarette smoking……………..….lung cancer Physical inactivity……………………...Obesity Non marital sexual behavior……………...STD

6 EPIDEMIOLOGY RESEARCH Explain why certain diseases are higher in some population groups than in others Modify the exposure levels in the high risk groups to reduce their excess burden of disease Identify specific Exposure (E) That might be causally related To a Disease (D) ED

7 AND TIME LINE STUDY DESIGN E D DESCRIPTIVE STUDY E D ANALYTICAL STUDY

8 Descriptive studies Correlation study Cross sectional study Case study i.e. correlation study is a cross sectional study in which the sample is the whole population STUDY DESIGN

9 E D Useful for generating a causal hypothesis Cross sectional study Both diseased and non diseased are studied Both D & E are measured They are measured as present or absent at single point in the time line It may be difficult to determine if E actually precede D in time Case study Case report Case series Descriptive studies

10 Analytical studies Observational studies Case-control Cohort Interventional studies (clinical trials) STUDY DESIGN E D ANALYTICAL STUDY

11 STUDY DESIGN There are two considerations regarding the study designs based on how D and E are handled by the investigator Does the E refer to some period in the subjects life before the occurrence of the D Is the sample being studied Selected on D basis or on E basis COHORTANALYTICALDISCRIPTIVECase-Control YESNOE D Sequence of research study

12 STUDY DESIGN There are two considerations regarding the study designs based on how D and E are handled by the investigator Does the E refer to some period in the subjects life before the occurrence of the D Is the sample being studied Selected on D basis or on E basis COHORTANALYTICALDISCRIPTIVECase-Control YESNOE D Intervention study is a cohort study in which the investigator decides who gets the E and who does not Sequence of research study

13 RANDOMIZATION definition A method based on chance alone by which study participants are assigned to a treatment group CHANCE

14 RANDOMIZATION benefits Eliminates the source of bias in treatments assignment Facilitates blinding the type of treatments to the investigator, participants, and evaluators Permits the use of probability theory to express the likelihood of chance as a source for the difference between outcomes

15 RANDOMIZATION types SIMPLE RESTRICTED BLOCKING STRATIFICATION MINIMIZATION

16 RANDOMIZATION types

17 BLINDING Single blind trial: The investigator is kept blind to the subjects assigned group. Double blind trial: The investigator and the subject are kept blind to the subjects assigned group Triple blind trial: Investigator, subject and assigners are kept blind to the subjects assigned group

18 BLINDING Investigator Assigner

19 BASIC MEASUREMENTS Math Ratio: a pair of numbers that compares two quantities Rate : When a ratio is used to compare two different kinds of quantities Proportion: is a statement that two ratios are equal (equal cross products) apples to oranges 3 to 6 3:6 ½ or half

20 Measures of Disease Frequency Incidence: No. of newly added disease cases in a population at risk during a specified time interval Prevalence: The proportion of individuals in a population who have disease at a specific point in time RATE RATIO measure of the instantaneous rate of disease useful in estimating length of time needed to follow up individuals measure the individual risk of disease useful in estimating the probability that an individual will be ill at a specific point in time

21 Measures of Disease Frequency Cumulative incidence: The proportion of people who become diseased during a specified period of time RATIO measure the individual risk of disease useful in estimating the probability that an individual will be ill at a specific point in time PREVALENCE = Incidence x Duration of disease

22 Measures of Disease Frequency prevalence Mortality And Remission Incidence or relapses graph

23 Measures of Disease Frequency equations Number of new cases of a disease during a given period of time * Total population at risk Number of new cases of a disease during a given period of time * Total person time of observation ** CI = IR = * Participants are observed till they get sick * Denominator is the total amount of disease-free person-time contributed by all individuals

24 A+B+C+D B+DA+C Total C+DD CNo A+BB AYes TotalNoYes DISEASE How to constr uct 2X2 TABLE

25 Uses Risk assessment Absolute risk Relative risk Attributable risk Odds ratio Screening test components Sensitivity Specificity

26 Risk assessment Involves people who develop disease due to an exposure Doesnt consider those who are sick but havent been exposed Absolute risk

27 A+B+C+D B+DA+C Total C+DD CNo A+BB AYes TotalNoYes DISEASE Absolu te risk 2X2 TABLE Absolute risk = A/A+B

28 Risk assessment Is the ratio of Prevalence of D in Exposed persons : Prevalence of D in non-Exposed persons A measure of strength of association between Exposure and Disease Relative risk RR = A/(A+B) C/(C+D) Relative Risk = Absolute risk in Exposed Absolute risk in non Exposed

29 A+B+C+DB+DA+CTotal C+DDCNo A+BBAYes TotalNoYes DISEASE Relati ve risk 2X2 TABLE

30 Risk assessment If RR = 1 Risk in exposed = Risk in unexposed ( no association ) If RR > 1 Risk in exposed more than in unexposed (positive association; causal) If RR < 1 Risk in exposed less than in unexposed (Negative association; protective) Relative risk interpretation

31 Odds ratio OR In case-control study participants are selected on the basis of D We dont know the incidence of D among exposed and non-exposed (A&C) The ratio of the odds of exposed developing disease to the odds of non-exposed developing the disease OR = =AD/BC A/C B/D

32 A+B+C+DB+DA+CTotal C+DDCNo A+BBAYes TotalNoYes DISEASE Odds ratio 2X2 TABLE

33 Risk assessment Is the mathematical difference between Prevalence of D in Exposed persons - Prevalence of D in A measure of excess occurrence of disease due to the exposure assuming that the exposure is causally related to the disease. Attributable risk AR = A/(A+B) - C/(C+D) Attributable Risk = Absolute risk in Exposed - Absolute risk in non Exposed non – exposed persons

34 Risk assessment Is the mathematical difference between Prevalence of D in Exposed persons - Prevalence of D in A measure of excess occurrence of disease due to the exposure assuming that the exposure is causally related to the disease. Attributable risk AR = A/(A+B) - C/(C+D) Attributable Risk = Absolute risk in Exposed - Absolute risk in non Exposed the whole population Population A+C/(A+B+C+D) non – exposed persons Absolute risk in the whole population

35 A+B+C+DB+DA+CTotal C+DDCNo A+BBAYes TotalNoYes DISEASE Attribut able risk 2X2 TABLE

36 Statistical association between E and D It may be valid in a given study, or there may be some alternative explanation for it: 1. Association might be due to chance 2. Association might be due to bias 3. Association might be due to confounding The smaller the sample size, the more room there is for chance to influence the study findings

37 BIAS Population Experimental Units Treatment Group Control Group Treatment No Treatment Result 34 2 1 An experiment or study is biased if it systematically favors a particular outcome 1. Subjects are not representative of the population 2. Treatment and control groups are inherently different on some lurking or confounding variable 3. Subjects are influenced by knowing they are in treatment or control groups 4. Evaluator of outcomes is influenced by knowing they are in treatment or control groups

38 Evaluating Bias in Epidemiological Study Definition: An incorrect estimate of the E / D relationship because some extraneous factor was not adequately controlled in the study Types of Bias: 1. Selection Bias 2. Information Bias a. Recall Bias b. Observer Bias 3. Non response Bias 4. Loss of follow up

39 How to Control Bias Blind data collector to avoid observer bias Mask the key E by asking many other useful questions to avoid information bias Ask close-ended questions to reduce recording errors by interviewer When assessing E history use multiple sources of information whenever possible Bias is a propriety of study design and not of a statistical analysis

40 CONFOUNDING Causation: change in X cause change in Y Common response: Both X and Y are responding to change in some other variable Z Confounding: the effect of X on Y cannot be distinguished from the effect of other variable Z on Y XY Z XYXY Z ? ? Causation Common response Confounding

41 Evaluating confounding in Epidemiological Study A confounding factor is a third variable associated with E under study and also independently affects risks of D E/D association is due to mixing of effects between E,D and a third variable Common confounding factors: age, sex and race Confounding can be positive or negative Randomization, restriction, matching and multivariable analysis are methods to control confounding in the study design and analysis respectively

42 SCREENING Is the application of a test to people who are asymptomatic for the purpose of classifying them to have particular disease Does not diagnose disease: persons who test positive are referred for more detailed diagnostic evaluation. Leads by early detection, before the development of symptoms to a more favorable diagnosis

43 SCREENING SENSITIVITY: Probability that a person who really has the disease will be classified as such (good positive) SPECIFICITY: Probability that a person who does not have disease will be classified as such (good negative) TEST

44 A+B+C+DB+DA+CTotal C+DDCNo A+BBAYes TotalNoYes DISEASE Sensitiv ity Specifi city 2X2 TABLE TRUTH TESTTEST Total B+DA+CA+B+C+DB+DA+C C+D A+B+C+DB+DA+C A+B C+D A+B+C+DA+C

45 DCNo BAYes NoYes Sensitiv ity Specifi city 2X2 TABLE TRUTH TESTTEST Sensitivity = A/A+C Specificity = D/D+B

46 DEFINITIONS Sensitivity = True positive True positive + False positive Specificity = True negative True negative + False positive

47 PREDICTIVE VALUE Predictive value of a positive test True positive True positive + False positive True negative True negative + False negative = Predictive value of a positive test =

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