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 BC 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