EPIDEMIOLOGY CLASS NOTES
1
EPIDEMIOLOGY NOTES
Chester Jones PhD.
I. Epidemiology-study of occurrence, distribution, & determinates of health & illness in a population.
A. Types of epidemiology:
1. Classical epidemiology—general analytic & descriptive study of health & disease.
2. Clinical epidemiology-diagnosis, & management of illness & the critical review of
literature.
B. Types of epidemiology:
1. Descriptive: Uses existing data to compare how mortality or morbidity may vary
among certain groups.
2. Analytic: More focused on the determinates of disease
C. Types of studies:
1. Retrospective—Looking back at previous morbidity or mortality & a disease. These
are less expensive & quicker to perform, but are subject to confounding & bias.
2. Case control—Compares the odds of past exposure to a suspected risk factor between
cases (CA--diseased individuals) & controls (CO—non-diseased people). Results in an
Odds Ratio (OR). Case controlled retrospective studies are the easiest & cheapest to do.
But they are prone to recall & other forms of bias.
3. Prospective—
a. Longitudinal—Study of a population over a (usually) long period of time. This
is the only way to determine incidence. Temporal relationship is clear. Bias &
confounding are easier to control. Only way to measure incidence. Expensive.
Not efficient for rare diseases.
b. Cohort—A group of healthy people are identified & followed for a specific
time. Exposed & unexposed participants are compared in relation to the disease
incidence. This type of study is time consuming & subjects are often lost & this
is not an appropriate for rare diseases. Bias & confounding are easier to control.
c. Cross-Sectional Studies—A sample of (or total) population is examined at a
given point-in-time. Takes a snapshot of a cohort.
d. Case-Controlled Studies—Most frequent method. Better for rare diseases &
those with long induction times. Efficient…but bias prone.
4. Experimental—to establish cause & affect relationships through control of variables.
C. Problems:
1. Research on humans is expensive, in some cases is unethical, difficult to control.
2. Diseases (especially chronic) are often insidious & difficult to link to a behavior or an
exposure.
3. Chronic diseases often require years of exposure before signs or symptoms surface
(latency period).
4. Humans are exposed to multiple risks over the course of their lives—sorting out the
one/s that are responsible for a disease is difficult. This is “confounding.” Confounding
may be controlled by:
a. Prevention (randomization, matching, & restriction).
b. Analysis (stratification & multivariate techniques).
5. The number of people with the disease may be small.
6. Bias can be reduced by standardization of forms & procedures, double-blind
techniques, & larger random samples.
EPIDEMIOLOGY CLASS NOTES
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D. Epidemiological Models:
1. Traditional Model:
AGENT
HOST
ENVIRONMENT
2. Health Field Concept:
BIOLOGY/HEREDITY
LIFESTYLE
HEALTH CARE SYSTEM
ENVIRONMENT
D. Terms & concepts:
1. Attributable risk—attributable risk among the population.
2. Ecological Fallacy—generalization of data.
3. Environment—physical (air pollution, sun exposure) & social/psychological (stress).
4. Etiological Fraction—how much of an exposure is attributable to the disease among
the population.
5. False Association—rates in the sample erroneously projected to the population
6. Incidence—new cases of a disease that occur during a study period. Incidence studies
start with healthy subjects.
7. Lifestyle—activities, behaviors, consumption patterns.
8. Morbidity—Illness rates (incidences of non-lethal flu).
9. Mortality—Death rates (number of deaths from heart attacks).
a. Infant mortality rate (age at which a child is no longer considered an infant
varies from country to country). In this country, we usually consider a child an
“infant” until one year of age.
b. Specific mortality rate—Race, gender, SES.
10. Prevalence—all cases of a certain disease that have occurred (may be retrospective).
a. Point prevalence: number of cases at a specific time.
b. Period prevalence: number of cases during a specific point in time.
c. Cumulative prevalence: cases at any time in the past (over a lifetime you will
average X number of colds).
11. Prevention—the goal is the prevention of morbidity and/or mortality
a. Primary prevention—prevent the disease from occurring (eradication of small
pox through vaccination).
b. Secondary—early detection & treatment of the disease (mammography).
c. Tertiary—rehabilitation and/or restoration of effective functioning after
occurrence of a disease (post-stroke rehab.).
EPIDEMIOLOGY CLASS NOTES
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12. Protective Characteristics-- a characteristic or behavior that prevents or protects a
person from developing a disease or condition.
a. BMI<40
b. Regular exercise
13. Risk Factor—a characteristic or behavior that places a person at risk for developing a
disease or condition.
a. Smoking
b. High cholesterol diet
14. Standardization—Direct used in vital statistics.
15. Standard population—a stable population (demographics my be derived from census
records.
E. Causality (clearer in single cause/single effect cases):
1. Temporal relationship—A causes B (A comes first). Example--rhinoviruses cause
colds.
2. Specificity: A cause leads to a single effect.
3. Strength or intensity—there is a strong relationship between findings. The correlation
coefficient between eating under-cooked eggs & the development of salmonella toxemia
is .85.
4. Consistency—Study after study arrive at the same findings. Schairere, et al (200),
Newcomb, et al (1995), Stanford, et al (1995), & Schuurman, et al (1995) have
determined that women taking hormone replacement therapy are at slightly increased risk
for the development of breast cancer.
5. Coherence—does the relationship make sense? Is there a true relationship between A
& B?
II. CRUDE MORTALITY RATE (CMR):
A. This rate expresses the actual observed mortality rate in a population & is considered the
starting point for the adjustment of rates. Crude Mortality Rate (CMR) determined by the
following formula:
Total Deaths
Crude Mortality Rate 
Total Population
B. Age-adjusted standardization (Direct & Indirect standardization)
1. Common set of weights = population (standardization is used to adjust rates to
account for differences in the population composition—i.e. age gender, ethnicity)
2. Direct: In direct standardization the calculation uses the standard population when
looking at the composition specific rates to determine the expected number of events.
COMMUNITY A
COMMUNITY B
Use this as a base—
add them together or
a common
denominator
3. Indirect—standardized mortality ratio. Common set of rates used in analytical
epidemiology—what would we expect (the number of cases of a disease then compare
Community A to the national standard. Indirect standardization—standard rates are used
& applied to the population & compared in order to calculate the expected number of
events & then compared to the observed number of events.
EPIDEMIOLOGY CLASS NOTES
C. Example: (Direct Calculation of Crude Mortality Rate)
AGE
COMMUNITY A
COMMUNITY B
POPULATION
DEATHS
DEATH RATE
PER 1,000
POPULATION
DEATHS
DEATH RATE
PER 1,000
1,000
3,000
6,000
13,000
7,000
20,000
50,000
15
3
6
52
105
1,600
1,781
15
1
1
4
15
80
CMRA=35.6
5,000
20,000
35,000
17,000
8,000
15,000
100,000
100
10
35
85
160
1,350
1,740
20
.5
1
5
20
90
CMRB=17.4
<1
1-14
15-34
35-54
55-64
>64
TOTAL
Crude Mortality Rate 
Community A
1,781
 1,000  35.6 / 1,000
50,000
Community B
1,740
 1,000  17.4 / 1,000
100,000
Total Deaths
Total Population
Calculation of expected Deaths In Age Groups & Age-Adjusted Death Rates
Age  Adjusted Death Rate  Population Age
Expected Death Rates 
1,000
AGE
STANDARD
POPULATION
(A+B)
DEATH
RATE IN A
PER 1,000
EXPECTED
DEATHS AT
A’s RATE
DEATH RATE
IN B
EXPECTED
DEATHS AT B’s
RATE
<1
1-14
15-34
35-54
55-64
>64
6,000
23,000
41,000
30,000
15,000
35,000
150,000
15
1
1
4
15
80
CMRA=35.6
90
23
41
120
225
2,800
3,299
20
.5
1
5
20
90
CMRB=17.4
120
11.5
41
150
300
3,150
3,772.5
TOTAL
Age  Adjusted Death Rates 
Community A
3,299
 1,000  21.99 / 1,000
150,000
Community B
3,772.5
 1,000  25.15 / 1,000
150,000
Expected Deaths
 1,000
Total Deaths( A  B)
X 
15  6000
 90
1,000
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EPIDEMIOLOGY CLASS NOTES
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Death Rate  Total Population
1,000
(Indirect Calculation of Expected Death Rate in Community A)
Calculated Expected Deaths 
AGE
POPULATION
OF A
STANDARD DEATH
RATE PER 1,000
EXPECTED DEATHS IN
A AT STANDARD RATE
<1
1-14
15-34
35-54
55-64
>64
1,000
3,000
6,000
13,000
7,000
20,000
50,000
20
.5
1
5
20
90
136.5
20
1.5
6
65
140
1800
2,032.5
TOTAL
III. ATTACK RATE: Incidence rate used to describe the occurrence of a disease.
A. Formula:
Total Number Ill
Attack Rate 
 100(%)
Total Number Ill  Well
B. Example: A local elementary school reported that there were 68 confirmed cases of head lice
among its 493 students. Determine the attack rate.
Attack Rate 
68
 100  13.79
493
1. The attack rate of head lice is therefore 13.79% of the student population.
2. “Head lice has been diagnosed in 13.79% of the student population.”
IV. INCIDENCE: Measure the rate at which people without a disease develop the disease during a
specific period of time. (Prospective)
A. Formula:
Incidence Rate 
Number of new cases over a specific time
Total population at risk of the disease in the same time period
B. Example: In a fictitious study 3,567 adolescent male subjects (ages 14-17) who use
smokeless tobacco products were followed for five years. During the study period, 283 of the
participants developed oral squamous cell carcinomas.
Incidence Rate 
283
 .07934  100,00  7933.84 per 100,000 per 5 year exp osure
3567
or
7933.84
 1589.77 per year exp osure
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EPIDEMIOLOGY CLASS NOTES
C. Incidence Density: Used to compensate for variations in observation periods for the study
subjects. The denominator becomes person-time of observation.
Incidence Density 
Number of new cases during the time period
Total person  time of observation
1. Once the person contracts the disease, then they are kicked out & no longer observed.
SUBJECT
NUMBER OF
MONTHS IN STUDY
A
B
C
D
E
F
TOTAL
5
6
2
4
9
12
Time in Study PERSONYEAR
Study Time
5/12
6/12
2/12
4/12
9/12
12/12
.4166
.5
.166
.33
.75
1
3.162
There were a total of three events.
3 (events)
x
.95


 95
3.162
100( person  years ) 100
person  years
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EPIDEMIOLOGY CLASS NOTES
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V. PREVALENCE: The number of people with a disease at a given point in time.
A. Formula:
Pr evalance Rate 
Total number of cases of a disease at a specific time
Total population at a given time
VI. RISK:
A. Problems—the risks of risks:
1.The people who are exposed will not develop the illness, but only have the probability
of doing so. For example, all those who smoke will not develop lung cancer.
2. Some people who are not exposed to the disease/risk factor will develop the disease.
For example, a very few cases of lung cancer are reported among those who have never
smoker, nor have been exposed to second-hand smoke.
B. Measures of Risk: Probability statements:
1. Absolute Risk: Synonymous with incidence & means the rate of occurrence of the
disease (prospective—incidence studies).
2. Relative Risk (Risk Ratio): Odds Ratio—epidemiological measures of the association
between exposure to a particular factor & risk of a certain outcome.
a. Formula:
Re lative Risk 
Incidence rate among those exp osed
Incidence rate among non  exp osed
b. Example: You are trying to determine the odds of developing Hepatitis A after
eating at Bubba Burger in Slapout, Alabama. You obtain a sample of 120
controls in the town population who have not eaten at Bubba Burger, you find
that 8 have positive Hepatitis A titers. Of the 56 interviewed patrons of Bubba
Burger you find that 12 have positive Hepatitis A titers.
c. Re lative Risk  1  (exp osed )  1(non  exp osed )
d. Relative Risk & attributable risk show an association between exposure to a
factor & risk of outcome.
3. Attributable Risk: Number or proportion of cases of illness or cause of death
attributable to an agent.
Attributable Risk 
Incidence of exp osed  non exp osed
 1,000
Incidence of exp osed
EPIDEMIOLOGY CLASS NOTES
VII. CASE CONTROL STUDIES:
M1
M0
N1
A. Case Exposure Rate (CAE):
N0
T
a
N1
B. Control Exposure Rate (COE):
b
No
C. Null Hypotheses in Case Control Studies:
1. The Null Hypothesis is retained if CAE & COE are (by differences in exposure rate)
very “close.”
2. The Null Hypothesis is rejected if the CAE & COE are very different.
3. No fast & firm guidelines exist for demarking “close” (defined by researcher).
a. Note: “Close” in epidemiology is defined the same way you define “close” in
horseshoes and hand grenades.
b. “The purpose of statistics is to serve the interests of The Party.” V. I. Lenin
c. To be confident, you will need to calculate the chi significance at the 95% CI.
D. In consideration of Odds Ratios—if the number 1 is included within the spread of the
confidence interval, then there is a significant difference in the Odds Ratio.
1. At the number 1, the risk of contracting the disease is 50:50 (equal risk).
2. If the Odds Ratio falls below one, the variable under consideration is considered to
have protective qualities.
3. Special attention should be placed on Odds Ratios that are close to the number 1.
4. When you state the odds, begin with stating the behavioral or environmental risk. For
example, “Among people who eat fish, the relative incidence of stomach cancer is 2.5
times greater than who not eat fish.”
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EPIDEMIOLOGY CLASS NOTES
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ODDS RATIO
.05
.1
.25
.5
1
1.5
2
2.5
3
A
B
C
D
a. Study A—activity found to be protective. Wide Confidence Interval hints at
low power (i.e. small sample).
b. Study B—activity found to be a risk factor for the disease. Narrow
Confidence Interval hints at high power (i.e. large sample).
c. Study C—activity found to be non-significant. Confidence Interval falls across
the number 1 (50:50 chance).
a. Study D—activity found to be a risk factor. Confidence Interval falls very
close to the number 1 (indicating that there may be only a weak association).
E. Attributable Risk: (PAR—Population Attributable Risk).
VIII. CHI-TEST:
A. Chi- () significance test is approximately equal to the Z-score.
1. Remember with an =.05 the Z-score = 1.96. & this is used to calculate the 95%
Confidence Interval.
2. The formula for :
a   M 1  N1
 T

N1 N 0 M 1 M 2
T 2 T  1
EPIDEMIOLOGY CLASS NOTES
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B. Calculations of Case-Control (Odds Rations & Chi Significance Tests): Working through
a problem: Linking diet high in fish to stomach cancer. You begin your study. You have 132
people who have stomach cancer. You interview them & find that 45 of them have diets high in
fish. You then gather a control group sample from the population of 146 people. After
interviewing them, you find that 25 of them eat diets high in fish. Now for the plot:
45
87
M1=70
121
M0=208
N0=146
N1=132
1. Case Exposure Rate (CAE): CAE 
25
T=278
a
45
CAE 
 .34 or 34%
132
N1
2. Control Exposure Rate (COE): COE 
b
25
COE 
 .17 or 17%
146
N0
3. Null:
a. The Null Hypothesis is retained if CAE & COE are close.
b. It is Null Hypothesis is rejected if CAE & COE are different.
ad
4. Odds Ratio (OR): OR  Relative Incidence( RI) 
bc
a  d 5445

 2.5
a. Or in our case: RI 
b  c 2175
b. So we say: “People who eat fish have a relative incidence of stomach cancer
2.5 times greater than those who do not.”
EPIDEMIOLOGY CLASS NOTES
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C. Chi-Test (used to determine if there is a significant difference): calculating for fish diet issue.
a   M 1  N1
 T


N1 N 0 M 1 M 2
T 2 T  1
1.
2.
3.
4.


132
45  .25132
11.76
278
 3.25
=
=
280,600,320
3.62
(132)(146)(70(208)
21,407,668
278 2 278  1
45  70


Z
An alpha level of .05 equals a Z-score of 1.96
If  > 1.96, there is a statistically significant difference at =.05
 = 3.25 is significant at the 95% Confidence Interval because it is >1.96
D. Confidence Interval calculations (for relative incidence):

1. RI
1
Z
X


= 2.51 1.96
Note: 2.5 is our Odds Ratio Point Estimate
3.25

RI  Po int Estimate
a. 2.51 .603 then…
b. 1  .603 = .4 & 1.6 then…
c. 2.50.4 (Input “2.5’ into calculator & then hit the “yx” key. Input 0.4 & then
push the “=” key. This will give you “1.442699906”
d. 2.51.6  4.3 (Input “2.5’ into calculator & then hit the “yx” key. Input 1.6 &
then push the “=” key. This will give you “4.33215527”
2. Or, with a Confidence Interval of 95% the score is somewhere between 1.4 & 4.3.
Because the number 1 does not fall within this spread, it is significant.
3. We say:
“Among fish eater, the best estimate is that they have a 2.5 times increased risk of
stomach cancer than non-fish eaters & that we are 95% confident that their true
range falls between 1.4 & 4.3.”
E. Attributable Incident Rate (AI)
1. Attributable Incident Rate Among Exposed (AIE):
OR  1 = 2.5  1  1.5  .60 or 60%
a. AI E % 
OR
2.5
2.5
b. We would then say, “Among people who eat fish, 60% of their stomach cancer
is attributable to their diet.”
2. Attributable Incident Rate Population (AIT):
a. AIT = (AIE%)(CAE) = (60%)(34%) = (.6)(.34) = .204 = 20%
b. We can then say, “If fish were eliminated from the diet, incidences of stomach
cancer would be reduced by 20% in the population.
EPIDEMIOLOGY CLASS NOTES
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IX. INTERACTION (CONFOUNDING):
A. Two or more variables may interact in a synergistic fashion to compound the risk of a
disease.
1. Disease 1 & 2 added together is much greater than the simple sum of each.
CAUSE #1
OR=2.2
OR=3.5
CAUSE #2
OR=12.3
DISEASE
2. For example: The odds ratio of contracting Black-Lung disease among coal miners
may be 2.2, but among miners who smoke the OR increases to 12.25. The joint effect is
more than the sum of the risks simply added together.
B. We will be using the following example: “What is the risk of mouth cancer among
alcoholics who smoke?”
1. 390 subjects are alcoholics (ETOH +). 110 are non-drinkers. Controls consist of 275
alcoholics & 225 non-drinkers.
FULL MODEL
CA
CO
390
275
a b
110
225
c d
500
500
N1
EXPOSURE
ETOH-YES
ETOH-NO
TOTALS
a. CAE 
N0
390
 .78 (78%)
500
b. COE 
275
 .55 (55%)
500
c. Relative Incidence: RI 
a  d 87750

 2.9
b  c 30250
TOTALS
500
M1
500
M0
1000
T
EPIDEMIOLOGY CLASS NOTES
d. Chi-Test:
a   M 1  N1
 T

N1 N 0 M 1M 0
T 2 T  1
=



390  .5500
=
62,500,000,000
999,000,000

390  500
500
1000
(500)(500)(500)(500)
10002 1000  1
140
62.56


140
 17.7
7.9
e.  = 17.7 is significant at the 95% Confidence Interval because it is > 1.96
f. RIˆ1 Z
X


= 2.91 1.96
Note: 2.9 is our Odds Ratio
17.7
2.91
.11
= 2.9-.89=2.6 & 2.9+1.11=3.3
g. We can say that, “People who drink alcohol are at 2.9 greater risk of mouth
cancer with a 95% confidence interval between 2.6 & 3.3.”
2. Now add smoking to the equation:
NON-SMOKERS
CA
CO
75
150
a b
50
150
c d
125
300
N1
EXPOSURE
ETOH-YES
ETOH-NO
TOTALS
RI 
N0
TOTALS
225
M1
200
M0
425
T
N0
TOTALS
440
M1
135
M0
575
T
a  d 11250

 1. 5
b  c 7500
SMOKERS
EXPOSURE
ETOH-YES
CA
315
ETOH-NO
60
TOTALS
375
CO
125
a
75
c
d
200
N1
RI 
b
a  d 23625

 3.15 (round to 3.2)
bc
7500
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EPIDEMIOLOGY CLASS NOTES
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a. Compare the ratios:
RINS=1.5
RIS=3.2
b. The ratio is from .5 to 2.2 because you take out “1” from both RI’s. You
subtract 1 because 1 stands for equal risk.
 2.2

 4.4 
c. Smokers have a 4 times greater risk of mouth cancers. 
 .5

ALCOHOL
+
+
SMOKE
+
+
SUBJECTS
Don’t smoke & don’t drink
Drink but don’t smoke
Smoke but don’t drink
Drink & smoke
CA
50 (b group)
75 (a1)
60 (a2)
315 (a3)
CO
150 (d group)
150 (c1)
75 (c2)
125 (c3)
RI
1.0
1.5
2.4
7.6
Those who smoke and drink are 7.6 times more likely to develop mouth cancer than those who neither
drink nor smoke.
ACHRONIMS
AIE%: Attributable Incident Rate Among Exposed

AIT%: Attributable Incident Rate Population (Etiological Fraction) RI  Po int Estimate
CA: Cases
CAE: Case Exposure Rate
CI: Confidence Interval
COE: Control Exposure Rate
OR: Odds Ratio

RI: Relative Incidence RI  Po int Estimate