1. RISK ASSESSMENT, Association and causation
Dr. MUSTAQUE AHMED
MBBS,MD(COMM MEDICINE),FELLOWSHIP

2. Objective of the class
To understand the meaning of RISK IN EPIDEMIOLOGY
To know the definition of risk and its uses
To know how to estimates RISK.
To understand the association and causation in epidemiology

3. DEFINATION OF RISK RISK means “the probability of some unwanted event”
RISK FACTORS: “Factors which is associated with an increased risk of acquiring disease”

4. USES OF RISK ASSESSMENT
1. PREDICTION: By calculating Risk , we use it to predict the future incidence of the disease
2.DIAGNOSIS: The presence of risk factors increases the probability that a disease is present.
3. PREVENTION: If a risk factor is a cause of disease, its removal can be used to prevent disease.

5. ESTIMATES OF RISK
Estimates of risk are obtained by observing the relationship between an exposure to the risk factor and the subsequent acquiring of the disease.
Calculating risks
The BASIC EXPRESSION OF RISK IS “INCIDENCE”
Of different epidemiological study designs available to test association between Risk factor and disease, the best design is cohort study as we can calculate incidence, RR,AR,PAR

6. 1. INCIDENCE: “The number of new cases of a disease arising in a defined period of time in a defined population”
A.incidence among exposed
B. incidence among not-exposed
C. incidence in general population.

7. 2/2 Contingency table for hypothetical cigarette smoking and lung cancer
Developed lung cancer
Did not develop lung cancer
total
Yes
70(A)
6930(B)
7000(A+B) NO
3(C)
2997(D)
3000(C+D)
Incidence rates
Among smokers=70/7000 X 1000 =10 PER 1000
Among non-smokers= 3/3000 X 1000 = 1 PER 1000
Among general population=73/10,000 X 1000 = 7.3 per 1000

8. 2. ATTRIBUTABLE RISK: (RISK DIFFERENCE) ADDITIONAL RISK
DEF: incidence of disease attributable to the exposure of a risk factor.
it is most appropriate method to know the about contribution of risk factor to disease and is expressed in percent.
CAL: I.E-I.O
X = ETIOLOGICAL FRACTION
I.E
Calculation: =Incidence among the exposed –incidence among not exposed
X 100
Incidence among the exposed
EG. 10-1
X 100 = 90 PERCENT 10
90 PERCENT OF LUNG CANCER AMONG SMOKERS WAS DUE TO THEIR SMOKING

9. 3.RELATIVE RISK(RISK RATIO)
R.R=I.E( INCIDENCE AMONG EXPOSED)
I.O( INCIDENCE AMONG NOT EXPOSED)
= 7/1 = 7
DEF: how many more times the likely exposed people will get a disease in relation to not-exposed. It is also called “the ratio of incidence in the exposed to the incidence in non-exposed.

10. 4.POPULATION ATTRIBUTABLE RISK(PAR)
DEF: It is the incidence of a disease in the population that would be eliminated if exposure were eliminated.
Cal= I.P(INCIDENCE IN GENERAL POPULATION)-I.O(INCIDENCE IN NOT-EXPOSED)
X100
I.P(INCIDENCE IN GENERAL POPULATION)
7.3-1/ 7.3 X 100= 86.3%
Eg. 86percent of deaths from lung cancer could be avoided if the risk factor of cigarettes were eliminated

11. EX.LUNG CANCER AND ITS RISK FACTOR
PRESENT
ABSENT
TOTAL
SMOKERS
100(A)
99900(B)
100000
NON-SMOKERS
8(C)
99992(D)
108(a+c)
199892(b+d)
200000
INCIDENCE OF DISEASE IN POPULATION(I.P) =108/ X1000 =0.54/1000
INCIDENCE OF DISEASE AMONG EXPOSED(I.E)=100/ X 1000 = 1/1000
INCIDENCE OF DISEASE AMONG NON-EXPOSED(I.0) =8/ X 1000= 0.08/1000
ATTRIBUTABLE RISK(A.R)=CAL: I.E-I.O
X 100 =1-0.08/1 X100 =92%
I.E
92 percent of lung cancer among smokers was due to their smoking
RELATIVE RISK=I.E/I.O = 1/0.08 =12.5
POPULATION ATTRIBUTABLE RISK =
CAL: I.P-I.0
X 100 = /0.54 X 100 =85.18%
I.P
Eg. 85percent of deaths from lung cancer could be avoided if the risk factor of cigarettes were eliminated

12. ASOCIATION AND CAUSATION
DESCRIPTIVE STUDIES- suggest an etiological hypothesis.
ANALYTICAL STUDIES: test the hypothesis derived from descriptive studies and find the causes of diseases.
AND EXPERIMENTAL STUDIES -confirm or refute the observed association between suspected cause and disease.
ASSOCIATION: Events are said to be associated when they occur more frequently together than one would expect by chance.

13. Causation and Association
Epidemiology does not determine the cause of a disease in a given individual
Instead, it determines the relationship or association between a given exposure and frequency of disease in populations
We infer causation based upon the association and several other factors
-- Therefore, an epidemiologic study cannot predict the exact cause of the disease in every individual
-- It looks at a population and tries to determine whether exposure is significantly associated to the disease on average - uses statistical techniques to make conclusions about the strength of these relationships
-- Often these relationships are more strongly supported/concluded when a plausible biological mechanism exists for the effect
-- In general, epidemiologic studies are not experimental - can’t expose humans deliberately to something that may affect their health, instead often look at populations that were inadvertently exposure to an agent due to job or where they live (clinical trials is exception)

14. Association vs. Causation
Association - an identifiable relationship between an exposure and disease
implies that exposure might cause disease
exposures associated with disease risk are often called “risk factors”
Most often, we design interventions based upon associations
Causation - implies that there is a true mechanism that leads from exposure to disease

15. General Models of Causation
Cause: event or condition that plays an role in producing occurrence of a disease
How do we establish cause in situations
that involve multiple factors/conditions?
For example, there is the view that most diseases are caused by the interplay of genetic and environmental factors.

16. Web of Causation There is no single cause
Causes of disease are interacting
Illustrates the interconnectedness of possible causes

17. Web of Causation - CHD Disease stress genetic susceptibility
medications
genetic susceptibility
smoking
lipids
Disease
gender
physical activity
Unknown factors
inflammation
blood pressure

18. Association can be broadly grouped into 3 categories:
Spurious association
Indirect association
Direct (causal) association
One – to – one causal relationship
Multi-factorial causation

19. Spurious association
Observed association between a disease and suspected factor may not be real.
E.g.:- A study in UK, showed high perinatal deaths are more in hospital births compared to home births.
it was not due to inferior quality of hospital facility but because hospital attract usually women who are at high risk for delivery ,therefore deaths are more in hospital then home delivery.

20. Indirect association:
It is a statistical association between a characteristic of interest and disease due to presence of a another factor known/unknown.
E.g.: Association between altitude and goiter
Iodine deficiency ALTITUDE
ENDEMIC GOITRE

21. Direct association
One – to – one causal relationship,
Multi-factorial causation
One to one causal relationship:-
Two variables (say A &B) are stated to be causally related if a change in A is followed by change in B, if doesn’t then their relationship cannot be causal.
EG. PERSON GETS INFECTION/disease if he gets infected with a particular agent.
Above concept was essence in Koch”s postulate. The proponents of germ theory of disease insisted that the cause must be
A-necessary
B-sufficient for the occurrence of disease
Pt. gets Tuberculosis if he gets infected with tuberculi bacilli in sufficient no and also depends upon host susceptibility.

22. Multi-factorial causation:-
Here in this case, many factors come in to account and act simultaneously and synergistically.
E.g.:-Obesity, hypertension and other factors are associated with myocardial infarction.
It Means the causes can effect alone and when associated with other causes. It acts synergistically.