Download presentation

2
**BASIC CONCEPTS IN EPIDEMIOLOGY**

Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University

3
**Epidemiology is the study of disease occurrence in human population**

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

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**

Identify specific Exposure (E) That might be causally related To a Disease (D) E D 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

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

8
**STUDY DESIGN 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

9
**Useful for generating a causal hypothesis**

STUDY DESIGN Descriptive studies 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 E D Useful for generating a causal hypothesis

10
**STUDY DESIGN Analytical studies Observational studies**

Case-control Cohort Interventional studies (clinical trials) E ANALYTICAL STUDY D

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 NO YES D E DISCRIPTIVE ANALYTICAL Case-Control COHORT 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 Intervention study is a cohort study in which the investigator decides who gets the “E” and who does not 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 NO YES D E DISCRIPTIVE ANALYTICAL Case-Control COHORT 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 subject’s assigned group. Double blind trial: The investigator and the subject are kept blind to the subject’s assigned group Triple blind trial: Investigator, subject and assigners are kept blind to the subject’s 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 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 measure of the instantaneous rate of disease useful in estimating length of time needed to follow up individuals RATE measure the individual risk of disease useful in estimating the probability that an individual will be ill at a specific point in time RATIO

21
**Measures of Disease Frequency**

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

22
**Measures of Disease Frequency**

graph Incidence or relapses prevalence Mortality And Remission

23
**Measures of Disease Frequency**

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

24
**DISEASE 2X2 TABLE EXPOSURE Yes No Total Yes A B A+B No C D C+D Total**

How to construct DISEASE Yes No Total Yes A B A+B No C D C+D EXPOSURE Total A+C B+D A+B+C+D

25
**2X2 TABLE Uses Risk assessment Screening test components 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**

Absolute risk Involves people who develop disease due to an exposure Doesn’t consider those who are sick but haven’t been exposed

27
**DISEASE 2X2 TABLE EXPOSURE Absolute risk = A/A+B Yes No Total Yes A B**

C D C+D EXPOSURE Total A+C B+D A+B+C+D Absolute risk = A/A+B

28
**Risk assessment Is the ratio of**

Relative risk 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 Absolute risk in Exposed Relative Risk = Absolute risk in non Exposed A/(A+B) RR = C/(C+D)

29
**DISEASE 2X2 TABLE EXPOSURE Yes No Total Yes A B A+B No C D C+D Total**

Relative risk DISEASE Yes No Total Yes A B A+B No C D C+D EXPOSURE Total A+C B+D A+B+C+D

30
**Risk assessment If RR = 1 Risk in exposed = Risk in unexposed**

Relative risk interpretation 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)

31
Odds ratio OR In case-control study participants are selected on the basis of “D” We don’t 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
**DISEASE 2X2 TABLE EXPOSURE Yes No Total Yes A B A+B No C D C+D Total**

Odds ratio DISEASE Yes No Total Yes A B A+B No C D C+D EXPOSURE Total A+C B+D A+B+C+D

33
**Risk assessment Is the mathematical difference between**

Attributable risk 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. non – exposed persons Absolute risk in Exposed Attributable Risk = Absolute risk in non Exposed AR = A/(A+B) - C/(C+D)

34
**Risk assessment Is the mathematical difference between**

Population Attributable risk 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. the whole population non – exposed persons Absolute risk in Exposed Attributable Risk = Absolute risk in non Exposed Absolute risk in the whole population AR = A/(A+B) - C/(C+D) A+C/(A+B+C+D)

35
**DISEASE 2X2 TABLE EXPOSURE Yes No Total Yes A B A+B No C D C+D Total**

Attributable risk DISEASE Yes No Total Yes A B A+B No C D C+D EXPOSURE Total A+C B+D A+B+C+D

36
**Statistical association between “E” and “D”**

It may be valid in a given study, or there may be some alternative explanation for it: Association might be due to chance Association might be due to bias 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 Subjects are not representative of the population**

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

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: Selection Bias Information Bias Recall Bias Observer Bias Non response Bias Loss of follow up

39
**How to Control Bias Blind data collector to avoid observer bias**

Bias is a propriety of study design and not of a statistical analysis 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

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 X Y X Y X Y ? ? Z 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 TEST 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)

44
**DISEASE TRUTH 2X2 TABLE TEST EXPOSURE Yes No Total Total Yes A B A+B**

Sensitivity Specificity TRUTH DISEASE Yes No Total Total Yes A B A+B A+B TEST No C D C+D C+D C+D EXPOSURE Total A+C A+C A+C A+C A+C B+D B+D B+D B+D A+B+C+D A+B+C+D A+B+C+D A+B+C+D

45
**TRUTH 2X2 TABLE Yes No Yes A B No C D TEST Sensitivity = A/A+C**

Specificity TRUTH Yes No Yes A B TEST No C D Sensitivity = A/A+C Specificity = D/D+B

46
**DEFINITIONS True positive Sensitivity = True positive + False positive**

True negative Specificity = True negative + False positive

47
**PREDICTIVE VALUE Predictive value of a True positive positive test =**

True positive + False positive Predictive value of a positive test True negative = True negative + False negative

Similar presentations

Presentation is loading. Please wait....

OK

Types of study designs Arash Najimi

Types of study designs Arash Najimi

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google