A brief introduction to
epidemiology
IMED
Medicine for Innovators and
Entrepreneurs
Rita Popat, PhD
Health Research and Policy
Division of Epidemiology
April 21, 2009
Outline
 What is epidemiology?
 Basic concepts
 Study designs
 Measures of associations
 Causality
 Applications using diabetes as an
example
2
Definition of Epidemiology
“Epidemiology is the study of the distribution
and determinants of health-related states or
events in populations and the application of
this study to control health problems”
- Last JM
3
Aims of Epidemiology
1. Determine risk factors of various diseases
2. Identify segments of the population with
highest risk to target prevention and
intervention opportunities
3. Evaluate the effectiveness of health
programs and services in improving health
of the population
4
Clinical epidemiology
Epidemiology/clinical research informs all of
the following:
 What diseases should you be looking for in patients?
 Which patients should you screen for disease?
 How are diseases are diagnosed?
 What does a disease diagnosis mean?
 What conditions cause the disease (risk factors)?
 How to prevent disease in your patients?
 How to treat diseases in your patients?
 What is disease prognosis?
 Public health policy/standard of practice.
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IMED: Project topics(subset)
 Design an improvement of device for continuous
glucose monitoring
 Design a device to predict and prevent nocturnal and
post-exercise hypoglycemia.
 Design an improvement on nutritional management of
diabetes.
 Identify a specific complication of diabetes (ie,
cardiovascular, renal, neurological, eye, etc) and
design an innovative approach to diagnosis or
treatment.
 Evaluate the current state of information on the
genetics of Type 2 diabetes and design an innovative
experimental or technical approach in this area.
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IMED: Project topics(subset)
 Design a novel or improved diagnostic test for insulin
resistance that can be used to screen for
undiagnosed type 2 diabetes.
 Propose an approach to identifying prognostic
markers for type 2 diabetes mellitus for a target
population of your choice.
 Design an approach to identifying genes involved in
diabetes susceptibility (type 1 or type 2) in a
population group with a high incidence of diabetes.
 Develop a project related to the issue of depression in
people with diabetes.
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Disease classification
Case definition or phenotype
General
Phenotypic classification
Presence or absence of clinical or pathologic
manifestations
Pathogenetic classification
Underlying pathophysiologic process
Specific
Etiologic classification
Underlying causal mechanisms
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Why is disease
classification important?
Disease heterogeneity
- Clinical heterogeneity influences diagnosis,
management, prognosis
- Etiologic heterogeneity (risk factors may
vary)
Diabetes
TYPE 1 Diabetes Mellitus
(T1DM)
TYPE 2 Diabetes Mellitus
(T2DM)
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THE LANCET • Vol 359 • January 5, 2002
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Descriptive epidemiology
 Frequency of disease
Prevalence: number of people in the population that
have the disease at a given point in time
Incidence: Rate at which new cases of disease appear
in the population (# of new cases / persons at risk)
Note:
Prevalence = Incidence x duration
 Who gets the disease? Frequency of
disease by location, ethnicity, age,
gender, and time period.
11
Descriptive epidemiology of
T1DM
 One of the most common childhood illnesses
 World-wide estimated 50,000 new cases annually
 In Caucasion populations: 1-3 per 1000 children
by the age of 20 years
 Prevalence in age group 1-15 yrs:
.05%-0.3%
 Approximately 123,000 persons aged 0-19 years
in the U.S. who currently have T1DM.
12
40
35
30
25
20
are about 1.5 times more likely
to develop TIDM as AfricanAmericans or Hispanics
Asian populations have the
lowest rates
15
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IDDM incidence per 100,000
Incidence of Type 1 diabetes
(ages 0-14
years)
in
the
area
American non-Hispanic whites
Why the geographic and ethnic variation?
a. Difference in susceptibility genes?
b. Differences in prevalence of causative environmental factors?
c. Combination of a and b?
13
Descriptive epidemiology of
T2DM (or NIDDM)
 A pandemic?
 Affects more than 16 million Americans and 135
million people worldwide
 Prevalence varies in different countries and
among different racial and ethnic groups
 In 2025, estimated worldwide prevalence among
adults 5.4%; 300 million adults are estimated to
have T2DM
 The majority of cases of T2DM in the future will
occur in developing countries (e.g., India and
China)
14
National Diabetes Surveillance
System
Incidence of diabetes
Prevalence of diabetes
Source: http://apps.nccd.cdc.gov/ddtstrs/
15
http://apps.nccd.cdc.gov/d
16
Risk factors
Factors that influence the risk or occurrence of
disease are called risk factors
(a) demographics (age, sex, race)
(b) genetic factors
(c) physical and biologic agents (drugs,
chemical agents)
(d) life-style factors (smoking, diet,exercise)
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Some important contributions of
epidemiology
Risk factor
Smoking
DES
Asbestos
OCs
Pesticides
Disease
……………………
……………………
……………………
……………………
……………………
Protective agents
Folate
Low dose
Aspirin
Lung cancer
Vaginal adenocarcinoma
Mesothelioma
Thromboembolic stroke
Parkinson’s disease
Disease
…………………… Neural tube defects
…………………… Stroke
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Cohort Studies
Disease
Exposed
Target
population
Disease-free
cohort
Disease-free
Disease
Not
Exposed
Disease-free
TIME
20
Cohort Studies
Exposed
Disease
T2DM (IE+)
Obese
Target
population
Disease-free
cohort
Not
Exposed
Normal wt.
Disease-free
Disease
T2DM (IE-)
Disease-free
TIME
21
Measures of association in a cohort study
Is exposure associated with disease risk?
 Relative Risk/Relative Rate (Risk Ratio)
RR = IE+ / IEUsual application: Search for causes
 Absolute difference : IE+ - IEUsual application: Primary prevention
impact
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Relative Risk (RR)
Exposure +
Exposure -
Diseased
+
a
c
Nondiseased
b
d
a
I E
a

b
RR 

c
I E
cd
a+b
c+d
Incidence
(probability) of
disease among
exposed
Incidence
(probability)of
disease among
unexposed
Relationship of physical activity vs BMI with
T2DM in women.
Design, Setting, and Participants:
 Prospective cohort study of 37,878 women free of
cardiovascular disease, cancer, and diabetes with 6.9
years of mean follow-up.
 Weight, height, and recreational activities were reported
at study entry. Normal weight was defined as a BMI of
less than 25; overweight, 25 to less than 30; and obese,
30 or higher. Active was defined as expending more
than 1000 kcal on recreational activities per week.
Main Outcome Measure: Incident type 2 diabetes,
defined as a new self-reported diagnosis of diabetes.
Weinstein et al. JAMA 292:1188-94
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Weinstein et al. JAMA 292:1188-94
Example: Body Mass index and risk of
clinical Type 2 diabetes in women
T2DM +
Obese[E+]
Normal wt. [E-]
T2DM-
762
5786
6548
178
19452
19630
0.116
762
/
6548
RR 

 12.6
178 /19452 0.0092
Interpretation: Obese women (BMI > 30 kg/m2) have an ~13-fold
increased risk of developing T2DM compared to women with normal
weight (BMI<25.0 kg/m2)
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Advantages/Limitations:
Cohort Studies
 Advantages:
 Allows you to measure true rates and risks of
disease for the exposed and the unexposed
groups.
 Temporality is correct (easier to infer cause and
effect).
 Can be used to study multiple outcomes.
 Prevents bias in the ascertainment of exposure
that may occur after a person develops a disease.
 Disadvantages:
Can be lengthy and costly! More than 50 years for
Framingham.
Loss to follow-up is a problem (especially if nonrandom).
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Case-Control Studies
Exposed
Disease
(Cases)
Not exposed
Target
population
Exposed
No Disease
(Controls)
Not Exposed
27
Measure of Association in
case-control studies: Odds Ratio (OR)
Exposure (E+)
Disease (D+)
Cases
a
No disease(D-)
Controls
b
c
d
No exposure(E-)
a+c
OR =
P ( E|D )
P ( E | D )
Odds of exposure
among cases
Odds of exposure
among controls
b+d
=
P ( E| D  )
P ( E | D  )
=
a a c
c a c
b b d
d b d
=
ad
bc
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Rare disease assumption: Odds ratio approximates the
Relative Risk
2X2 Contingency table set up from a cohort study
Diseased
Exposure +
Exposure -
Non-diseased
a
b
a+b
c
d
c+d
Rare disease
assumption
a
a
a
P( D | E )
ad
( a  b)  b
a

b
b
RR 




c
c
c
P ( D | E )
bc
cd
(c  d )  d
d
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History of t2dm in a first degree relative and disease
risk
T2DM Cases
Controls
Family history +
346
165
Family history -
391
620
737
785
(346)(620)
OR 
 3.325
(391)(165)
Interpretation: History of T2DM in a first degree relative is
associated with a 3.3-fold increased risk of T2DM
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Meigs J et.al. JAMA. 2004;291:1978-1986.
 Nested case-control design
 a case-control study nested within a cohort study
Nurses’ Health Study
(cohort)
to: 1976
total n=121,700
bld samples n=32,826
in 1989-1990
Cases
n=737 incident
T2DM
Controls n=785
(matched on age,
race, fasting status)
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Endothelial Dysfunction and Risk of Type 2 Diabetes
Do these data suggest that risk of T2DM increases as levels of Eselectin increase?
Yes, the risk of T2DM increases as E-selectin levels increase (aka
dose-response effect).
Meigs, J. B. et al. JAMA 2004;291:1978-1986.
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Advantages and Limitations:
Case-Control Studies


Advantages:
 Cheap and fast
 Great for rare diseases
Disadvantages:
Exposure estimates are subject to recall bias (those
with the disease are searching for reasons why they
got sick and may be more likely to report an
exposure) and interviewer bias (interviewer may
prompt a positive response in cases).
Temporality is a problem (did exposure cause
disease or disease cause exposure?)
33
Putative Risk factors for T2DM: Genetic factors
T2DM risk
(i) Family history…………………… 2-6 fold  risk if a
(genetic+nongenetic factors)
parent/sibling has T2DM
(ii) Twin studies……………………  concordance in MZ twins
(iii) Genes
……………………
?
(T2DM is a complex genetic disorder)
(iv) Race/ethnicity
Prevalence of T2DM (%)
40
35
30
25
20
15
10
5
0
Non-Indian
Half-Indian
Extent of Pima Indian heritage
Full-Indian
Fat-mass and obesityassociated (FTO)
gene: OR for t2DM
~1.25
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Putative Risk factors for T2DM:
Non-Genetic factors
T2DM risk
(i) Obesity
Body Mass Index
Waist-to-hip ratio
……………………
……………………


(ii) Physical Activity
……………………

(iii) Gestational diabetes ……………………

……………………

(v) Inflammation
……………………

(vi) Dietary factors
……………………
(iv) Low birth weight
(reduced beta-cell mass)
difficult to summarize!!
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.
N Engl J Med 2008;359:2220-32
Background
Type 2 diabetes mellitus is thought to develop from an
interaction between environmental and genetic factors. We
examined whether clinical or genetic factors or both
could predict progression to diabetes in two prospective
cohorts.
Genetic factors:
Clinical risk factors:
Variants in 16 genes: TCF7L2,
Family history of diabetes
KCNJ11, PPARG , CDKAL1,
IGF2BP2, DKN2A/CDKN2B FTO,
BMI, Age, Gender, Impaired
HHEX, SLC30A8, WFS1, JAZF1,
insulin secretion and action
CDC123/CAMK1D, SPAN8/LGR5,
and others….
THADA, ADAMTS9 and NOTCH2
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.
N Engl J Med 2008;359:2220-32
Conclusions
As compared with clinical risk factors alone, common
genetic variants associated with the risk of diabetes had a
small effect on the ability to predict the future development
of type 2 diabetes. The value of genetic factors increased
with an increasing duration of follow-up.
38
Modifiable factors and T2DM
Percentage of type 2 diabetes that is potentially
preventable by life-style modifications.
T2DM: Low-risk definition
•body mass index <25 kg/m2
•physical activity equivalent to >30
min per day of brisk walking
•Good diet (e.g., low in saturated
and trans-fat, fiber)
•moderate alcoholic consumption
•Nonsmoking
Source: Willet WC. Science, 2002
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Intervention Studies: evaluating therapies
for treatment or prevention
Disease
Intervention
group
Target
population
Disease-free
cohort
No
intervention
group
Disease-free
Disease
Disease-free
TIME
41
Stages in testing new therapies
Phase I
Unblinded, uncontrolled studies in a few
volunteers to test safety
Phase II
Relatively small controlled clinical studies
to evaluate drug effectiveness, short term
side effects
Phase III
Relatively large, randomized, controlled,
blinded trials to test effect of therapy on
clinical outcomes
Phase IV
Large trials or observational studies after
drug is approved by FDA to assess rate
of serious side effects and other uses
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Intervention Studies/Clinical trials
 Advantages:
 Allows randomization (controls for confounding)
 Allows double-blind assessment (controls bias)
 Disadvantages:
• Can be lengthy and expensive
• Loss to follow-up is a problem (especially if nonrandom)
• Addresses a narrow clinical question
• Ethical limitations
43
(N Engl J Med 2002; 346:393-403.)
44
(N Engl J Med 2002; 346:393-403.)
45
(N Engl J Med 2002; 346:393-403.)
46
Best
Experiment/RCT
Prospective cohort
Cost and
ease
Retrospective cohort
Case-control
Correlation/x-section
Proof
of cause
Case series
Case report
Best
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Causation in Observational
Studies
 Association does not prove causation
 If a putative risk factor and the occurrence of an
outcome are strongly associated with each other it
does not provide evidence that the risk factor causes
the disease, only implies that it is correlated with
outcome
 Non-causal explanations may cause a spurious
association – study biases (measurement error,
selection bias, confounding)
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Confounding
Confounding is the defined as a distortion of
an exposure-outcome association brought
about by the association of another factor
with both the outcome and exposure
Exposure
(e.g.,
depression)
Outcome
(e.g.,T2DM)
Confounding
factor
(e.g., physical
activity )
49
Depression and risk of T2DM
JAMA. 2008;299(23):2751-2759
50
Depression and risk of T2DM
JAMA. 2008;299(23):2751-2759
51
Selection bias
 A form of sampling bias due to systematic
differences between those who are selected
for study (or agree to participate) and those
who are not selected (or refuse to
participate).
Information bias (measurement error)
 Erroneous classification of disease status or
exposure into a category other than that to
which it should be assigned.
52
Study designs and biases
Threats to
internal validity
Case-control
Cohort
RCT
Confounding
Generally present
Generally
present
Not likely (due to
randomization)
Selection bias
Likely (e.g., when May occur due to May occur due to
low response
differential loss to differential loss to
rates)
follow up
follow-up
Misclassification More likely to be
differential
of exposure
Generally nondifferential
Most likely nondifferential
Most likely nondifferential
Misclassification
of outcome
Not likely; if exists
then nondifferential (e.g.,
drop-in/drop out)
Most likely nondifferential
Diagnostic tests
The purpose of a diagnostic test is to move the
estimated probability of the presence of a
disease toward either end of the probability
scale.
Rule out
0
Rule in
100
Almost all approaches to gathering clinical
information can be considered as tests (e.g.,
history, physical exam)
54
Accuracy of Diagnostic tests
True classification of disease status (gold
standard)
Present
Absent
Positive
(present)
Diagnostic
Test result
Negative
(absent)
A
True positives
C
False negatives
A+C
B
False positives
D
A+B
C+D
True negatives
B+D
Sensitivity and specificity are terms used to
describe the validity (accuracy) of the
diagnostic test relative to the gold standard.
55
Accuracy of Diagnostic tests
Sensitivity: proportion of persons with the disease of
interest who have a positive test result
Sensitivity = TP/(TP+FN)=A/(A+C)
Specificity: proportion of persons without the disease of
interest who have a negative test result
Specificity = TN/(FP+TN)= D/(B+D)
56
Accuracy of Diagnostic tests
57
Background—The BD Logic® (Becton, Dickinson and Co.;
Franklin Lakes, NJ) and FreeStyle® (Abbott Diabetes Care;
Alameda, CA) meters are used to transmit data directly to
insulin pumps for calculation of insulin doses and to calibrate
continuous glucose sensors as well as to monitor blood
glucose levels.
Methods—The accuracy of the two meters was evaluated in
two inpatient studies conducted by the Diabetes Research in
Children Network (DirecNet).
Meter glucose measurements made with either venous or
capillary blood were compared with reference glucose
measurements made by the DirecNet Central Laboratory.
58
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Summary
 Measures of disease occurrence (prevalence and
incidence)
 Study designs for evaluating role of risk factors in disease
etiology: genetic and non-genetic factors
 Cohort
 Case-control
 Study designs for testing interventions/therapy
 RCTs
 Evaluating utility of diagnostic tests, devices, or
biomarkers for diagnosis or prognosis
 Accuracy – sensitivity, specificity
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Contact information
Rita Popat, PhD
Clinical Assistant Professor
Office: HRP Redwood Building, Rm T209
Tel: 650-498-5206
E-mail: rpopat@stanford.edu
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