BASIC CONCEPTS IN EPIDEMIOLOGY Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University.
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Transcript of BASIC CONCEPTS IN EPIDEMIOLOGY Dr. Yasser Abdelrahman Lecturer Of Anesthesia Ain Shams University.
BASIC CONCEPTS IN EPIDEMIOLOGY
Dr. Yasser AbdelrahmanLecturer Of Anesthesia Ain Shams
University
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
The only medical subspecialty that is concerned with the occurrence of illness over time
WHAT IS EPIDEMIOLOGY
TIME 1
Disease absentDisease present
or absent
TIME 2
Fundamental Assumptions
Human disease Does not occur at random Has causal and preventive factors Is a consequence of specific exposures
Environmental, Biological Behavioral
Radiation…………………….…………..CancerReduced fluoride…………..……Dental carriesSecond hand smoke…….Respiratory disease
Viruses………………………………....MeaslesBacteria………………………...…..Pneumonia
Cigarette smoking……………..….lung cancerPhysical inactivity……………………...ObesityNon marital sexual behavior……………...STD
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)
E D
AND TIME LINE
STUDY DESIGN
ED
DESCRIPTIVE STUDY
ED
ANALYTICAL STUDY
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
STUDY DESIGN
ED
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
Analytical studies Observational studies
Case-control Cohort
Interventional studies (clinical trials)
STUDY DESIGN
ED
ANALYTICAL STUDY
STUDY DESIGN
There are two considerations regardingthe study designs based on
how “D” and “E” are handledby the investigator
Does the “E” refer to some periodin the subjects life beforethe occurrence of the “D”
Is the sample being studiedSelected
on “D” basis or on “E” basis
COHORTANALYTICALDISCRIPTIVE Case-Control
YESNO ED
Sequence of research study
STUDY DESIGN
There are two considerations regardingthe study designs based on
how “D” and “E” are handledby the investigator
Does the “E” refer to some periodin the subjects life beforethe occurrence of the “D”
Is the sample being studiedSelected
on “D” basis or on “E” basis
COHORTANALYTICALDISCRIPTIVE Case-Control
YESNO ED
Intervention study is a cohort study in which the investigator decides who gets the “E” and
who does not
Sequence of research study
RANDOMIZATIONdefinition
A method based on chance alone by which study participants are assigned to a treatment
group
CHANCE
RANDOMIZATIONbenefits
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
RANDOMIZATIONtypes
SIMPLESIMPLE RESTRICTEDRESTRICTED
BLOCKINGBLOCKING STRATIFICATIONSTRATIFICATION MINIMIZATIONMINIMIZATION
RANDOMIZATIONtypes
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
BLINDING
Investigator
Assigner
BASIC MEASUREMENTSMath
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
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
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
Measures of Disease Frequency
prevalence
Mortality
AndRemissi
on
Incidence or
relapses
graph
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
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASEHow to
construct
2X2 TABLE
2X2 TABLE
Uses Risk assessment
Absolute risk Relative risk Attributable risk Odds ratio
Screening test components Sensitivity Specificity
Risk assessment
Involves people who develop disease due to an exposure
Doesn’t consider those who are sick but haven’t been exposed
Absolute risk
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASEAbsolute risk
2X2 TABLE
Absolute risk = A/A+B
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 ExposedAbsolute risk in non Exposed
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASERelative risk
2X2 TABLE
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
Odds ratioOR
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/BCA/CB/D
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASEOdds ratio
2X2 TABLE
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
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
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASEAttributable risk
2X2 TABLE
Statistical associationbetween “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
BIAS
Population ExperimentalUnits
Treatment Group
Control Group
Treatment
No Treatment
Result
Result
3 421
An experiment or study is biased if it systematically favors a particular outcome
1. Subjects are not representative of the population2. Treatment and control groups are inherently
different on some lurking or confounding variable3. Subjects are influenced by knowing they are in
treatment or control groups4. Evaluator of outcomes is influenced by knowing
they are in treatment or control groups
Evaluating Bias inEpidemiological 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
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 studydesign and not of a statistical analysis
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
Z
X Y X Y
Z
?
?
Causation Common response Confounding
Evaluating confounding inEpidemiological 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
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
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
A+B+C+DB+DA+CTotal
C+DDCNo
A+BBAYes
TotalNoYes
DISEASESensitivity
Specificity
2X2 TABLE
TRUTH
TEST
Total
B+DA+C A+B+C+DB+DA+C
C+D
A+B+C+DB+DA+C
A+B
C+D
A+B+C+DA+C
DCNo
BAYes
NoYes
Sensitivity
Specificity
2X2 TABLE
TRUTH
TEST
Sensitivity = A/A+C
Specificity = D/D+B
DEFINITIONS
Sensitivity= True positive
True positive + False positive
Specificity= True negative
True negative + False positive
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
=