Epidemiology.ppt

Epidemiology lecture notes
for MPH students
Temesgen Gebeyehu (MPH and Assistant Professor of Epidemiology)
email : tomigeb2006@gmail.com , November, 2022

Course information
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 Course title: Epidemiology
 Course code: PHEP 521
 Credit hours: 3
Course description:
 The main objective of the course is to equip students
with an understanding of basic, intermediate and
advanced concepts of epidemiology and skills in
quantitative techniques to plan, design and undertake
basic epidemiological studies on specific health
problems.
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Course content
3
 Concepts and history of epidemiology
 Basic tools of measurements in epidemiology
 Measures of morbidity and mortality
 Epidemiological study designs
 Case report and case series
 Ecological studies
 Cross sectional study design
 Case-control study design
 Cohort study design
 Experimental design
 Outbreak investigation/surveillance/
 Screening and evaluation of diagnostic test
 Errors in epidemiological studies
 Concept of causality
 Ethical issues in research
 Systematic review and meta-analysis
 Critical review and scientific writing
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Mode of delivery and evaluation
4
Mode of delivery
 Lecture
 Discussion
 Individual assignments
Mode of evaluation
 Research critique on a study design and
measures of association 50%
 Final exam 50%
 Total 100%
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References
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1. Principles of EPIDEMIOLOGY Second Edition, Public
Health Service Centers for Disease Control and
Prevention (CDC)
2. Understanding the Fundamentals of Epidemiology an
evolving text by Victor J. Schoenbach, Ph.D. with Wayne
D. Rosamond, Ph.D.
3. Introduction to Epidemiology, by Lucianne Bailey,
Katerina Vardulaki, Julia Langham and Daniel
Chandramohan.
4. Basic epidemiology, 2nd edition, by R Bonita, R
Beaglehole, and T Kjellström
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Epidemiology
Epidemiology was derived from the word
“Epidemic”
Epi = Among
Demos = People
Logos = Study
A very old word dating back to 3rd Century
B.C.
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Cont’d
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 Classically Epidemiology is defined as the
study of the frequency, distribution and
determinants of diseases and health-related
problems in human populations and the
application of this study to the control of health
problems.
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Important terms to understand the concept of
Epidemiology
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 Frequency: This deals with the quantification of the occurrence
of illness and mortality.
 Distribution: This includes not only geographical distribution of
the condition but also distribution in time (example seasonal
fluctuation), and according to the type of person most or list
affected (TPP).
 Determinants: This mainly answers the question why specific
individuals are affected/not affected by the condition? What "risk
factors" are "causing" this?
 Health related problems: This includes injuries, vital events,
health related behaviors and pertinent social factors.
 Application: Epidemiology is an applied science
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Important terms Cont’d
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 Risk Factor: An individual attribute (intrinsic
characteristic of the individual) or exposure (external
environmental situation) that is positively or negatively
associated with the occurrence of a disease. This is also
known as predisposing factor.
 Cause: That combination of risk factors which, act alone
or in combination, at some time during an individual’s life,
inevitably result in a particular disease in that individual.
 Cause is/are necessary for the disease to occur and
in their absence the disease can’t occur.
 It is also known as Etiological Agent.
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Steps in the paradigm of public health
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 Define the problem
 Measure its magnitude
 Understand the key determinants
 Develop intervention/prevention strategies
 Set policy/priorities
 Implement and evaluate
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Classification of Epidemiology
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1. Descriptive Epidemiology
2.. Analytic Epidemiology
3. Experimental Epidemiology
4. Evaluative Epidemiology
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Subdivisions of Epidemiology
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 Infectious disease epidemiology,
 Clinical epidemiology,
 Genetic epidemiology,
 Nutritional epidemiology,
 Reproductive epidemiology,
 Environmental epidemiology,
 Occupational epidemiology,
 Social epidemiology, etc.
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Why we study epidemiology?
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Epidemiology provides a comprehensive information
for:
 Understanding of diseases,
 Identification of priority health problems,
 Hypothesis generation and testing,
 Designing of disease control and prevention strategies,
 Evaluation of health programs,
 Provision of evidence based care.
It provides the methodology for understanding and
planning, conducting, analyzing, interpreting and
presenting of scientific data.
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Basic Assumptions of Epidemiology
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 The two fundamental assumptions of
epidemiology are:
1. Human disease doesn’t occur at random
2. Human disease has causal and preventive factors
that can be identified through scientific
investigation.
 Unique Characteristics of Epidemiology
1. Studies are conducted on human population,
2. Allows the quantification of the magnitude of
relationship between exposure and disease,
3. Provide information on how to prevent disease
and alter risk through interventions.
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History and evolution of Epidemiology
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 Hippocrates (460-477 BC): The epidemiologic way
of thinking originated in writings ascribed to the Greek
philosopher-physician Hippocrates in the fifth century.
Hippocrates, in his book entitled Air, Waters and
Places, displayed an extraordinary/unusual
awareness of the impact of environment (geography,
water, climate, and housing) on personal well-being.
His main concept was that disease causality can be
attributed to climate, seasonal variations and location.
He noted that habits, regimens and personal pursuits
are all factors associated with disease occurrence.
Physical environment is associated with human
disease.
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History and evolution…
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 Claudius Galen (129-199): Known as father of
Experimental Physiology. His anatomical studies on
animals and observations of how the human body
functions dominated medical theory and practice for
1400 years. Galen simply studied the bodies of animals
to support his research and compare them to human
anatomy.
 In epidemiology he described the importance of “innate
qualities of human body”, lifestyle and personality in
disease causation.
 He also argued that diseases were caused by Miasma
(Bad air) from waste, stagnant water and dead animals.
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 Gerolamo H Francastorius (1478-1553): in his writing
(1546), presented the first clear and coherent germ
theory of disease, he theorized that a variety of
diseases are caused by transmissible, self-propagating
entities.
 He thought that these agents were specific to each
disease and could spread from person to person or
through infected articles (fomites) or at a distance.
 He went as far as arguing that treatment should consist
of the destruction of the entities by either heat or cold or
of their evacuation from the body, or by neutralizing
them by antagonistic substances.
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 John Graunt (1620–1674): John Graunt is regarded
as the founder of Demography.
 He was the first to take a major step forward to
quantify patterns of birth, death and disease
occurrence. high infant mortality, male-female, and
urban- rural differences & seasonal variations
 His observations upon the Bills of Mortality (1662)
were based on a series of weekly bills covering
individual deaths and their causes in the London area
back to 1603.
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 Thomas Sydeham (1624 – 1689): Insisted that
observation should have precedence over theory in
the study of the natural history of diseases.
 Dr. Sydeham wrote how he observed diseases without
allowing various traditional theories of disease
influence his observation and work. In doing so he
identified and recognized various diseases. Among his
many achievements was the discovery of a disease,
Sydenham's Chorea, also known as St Vitus
Dance which is a neurological disorder of childhood
resulting from infection via Group A beta-hemolytic
streptococcus (GABHS), the bacterium that causes
rheumatic fever. February 2, 2024
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 James Lind (1716-1794): in 1747 he carried out
experimental study on the etiology and treatment of
scurvy.
 While he was a British navy physician, he observed
sailors were developing a deadly bleeding disorder
(scurvy) on long voyages. He demonstrated
experimentally that citrus fruits like orange and
lemon can cure scurvy.
 Noah Webster (1758-1843): Argued that epidemics
were related to certain environmental factors.
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 Pierre Charles Alexandre Louis (1787-1872): He
was a French physician, known for introducing the
use of the "numerical method" in the field of
medicine.
 He argued that the concept that knowledge about a
disease (its history, clinical presentation and
treatment) could be derived from aggregated patient
data.
 Louis became known for his research on
tuberculosis and typhoid fever. He also numerically
demonstrated that bloodletting was an ineffective
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 William Farr (1807–1883): In 1838 Farr introduced
a national system of recording causes of death in
Great Britain.
 He himself first analyzed the data with great skill. He
was instrumental in building up a classification of
diseases for statistical purposes.
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 John Snow (1813–1858): He is known as the father of Modern
Epidemiology. He investigated the two epidemics of Cholera
occurred in London in 1849 and 1854.
 John Snow first suspected the occurrence of Cholera has
something to do with the drinking water source of the city.
 Then he compared the Cholera mortality pattern across two
drinking water sources of London.
 He found out that the mortality rate was 20 times lower for the
districts supplied by the Lambeth Company in respect to those
supplied by Southwark and Vauxhall Company.
 In doing so he demonstrated how hypothesis can be generated
from descriptive epidemiology and how it can be tested and
applied for public health benefit.
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John Snow—The broad street pump
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 Joseph Goldberger (1874–1929): He was a well
known advocate for scientific and social recognition
of the links between poverty and disease.
 In 1914 he investigated the cause of Pellagra and
demonstrated the disease was associated with diet
(deficiency of niacin or vitamin B3) rather than
infection agents.
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Janet Elizabeth Lane-Claypon (1877–1967):
 She was an English physician and one of the founders
of the science of epidemiology, pioneering the use of
Cohort studies and Case-control studies.
 In 1912, she published a ground-breaking study of two
cohorts (groups) of babies, fed cow’s milk and breast
milk respectively to demonstrate the importance of
breastfeeding for child growth.
 In 1923, she studied factors associated with breast
cancer by comparing 500 women with a history of
breast cancer with another 500 women who were free
of the disease. The technique nowadays is known as
‘Case Control Study’. February 2, 2024
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 Richard Doll (1912–2005) and Bradford Hill (1897-
1991): in 1950 they showed that smoking was an
important cause of lung cancer.
 Richard Doll did pioneering work on the relationship
between:-
 radiation and leukemia
 asbestos and lung cancer, and
 alcohol and breast cancer.
 Bradford Hill pioneered the randomized clinical trial
and in 1965 he detailed criteria for assessing evidence
of causation.
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 Thomas Dawber and William Kannel (1947):
Framingham Study is notable for bringing about a
shift in focus of epidemiology from infectious to
noninfectious diseases.
 It is indisputably the foundation stone for current
ideas about risk factors in general and the
prevention of ischemic heart disease in particular.
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Streptomycin Tuberculosis Trial (Late 1940’s):
 The British Medical Research Council conducted
one of the first modern experimental studies on the
use of streptomycin to treat pulmonary tuberculosis.
 The study was randomized and controlled trial which
acted as a base for further clinical trials.
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Epidemiologic Transition
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 The concept of Epidemiologic Transition deals with the
characterization of long-run changes in cause of
death.
 It was first publicized by the public health physician
Abdel R. Omran in 1971.
 " Omran wrote, "a long shift occurs in mortality and
disease patterns whereby pandemics of infection are
gradually displaced by degenerative and man-made
diseases as the chief form of morbidity and primary
cause of death"
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Epidemiologic transition has three stages:
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 The stage before the transition: “The Age of Pestilence
and Famine” when mortality is high and fluctuating, thus
precluding sustained population growth. Average life
expectancy at birth is low and variable, in the range of 20 to
40 years.
 The transitional stage: “The Age of Receding Pandemics”
when mortality declines progressively and the rate of decline
accelerate as epidemic peaks become less frequent or
disappear. The average life expectancy at birth increases
steadily from about 30 to 50 years. Population growth is
sustained and begins to describe an exponential curve.
 The stage after the transition: "The Age of Degenerative
and Man-Made Diseases” when mortality continues to
decline and eventually approaches stability at a relatively low
level. The average life expectancy at birth rises gradually
until it exceeds 50 years. It is during that stage that fertility
becomes the crucial factor in population growth".
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Natural History of a Disease
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Natural History of a Disease Cont’d
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 The scientific process of studying natural history of a
disease is difficult due to the following two reasons:
1. The scientific objective of observing the natural
history of a disease is not ethically acceptable.
2. Long term follow-up studies might be needed to
clearly describe the natural history of chronic
diseases.
 As a result natural history is usually pieced or
assembled together from mixture of observations.
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Level of Disease Prevention
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1. Primordial Prevention
2. Primary prevention
3. Secondary prevention
4. Tertiary prevention
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Infectious Disease Epidemiology
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 The study of epidemiology of infectious disease is
important due to:
1. Changes in pattern and determinants of infectious
diseases
2. Discovery of new infectious diseases /Ebola, SARS,
Zika/
3. The possibility that some chronic diseases have an
infective origin.
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Levels of Disease Occurrence
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 Endemic: A persistently low to moderate level of occurrence
of a disease.
 Hyperendemic: A persistently high level of occurrence of a
disease.
 Sporadic: Occasional cases occurring at irregular interval.
 Epidemic: The occurrence of any health related condition in a
given population in excess of the usual frequency in a given
period.
 Pandemic: An epidemic spread over several countries or
continents.
 Outbreak: An epidemic restricted in place, person and time.
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The Epidemiologic Triad (Agent, Host factors and
Environment)
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Terms Commonly used to Describe Time Course of Disease
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 Incubation period
 Induction Period
 Pre patent period
 Infectious period
 Latent period
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Chain of Infection
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Basic Reproductive Number (Ro)
•Ro Refers to the mean number of individuals directly infected
by an infectious case through the total infectious period,
when introduced to a susceptible population.
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Ro=basic reproductive number
Ro= P.C.D, where:
p: probability of transmission per contact
c: contacts per unit time
d: duration of infectiousness
•If R0 > 1, then disease will increasingly spread (epidemic)
•If R0 = 1 then disease will become stable (endemic)
•If R0 < 1 then disease will die out(disappear)

 The herd immunity threshold is the proportion of a
population that need to be immune in order for an
infectious disease to become stable in that
community.
 If this is reached, for example through immunization,
then each case leads to a single new case (R=1)
and the infection will become stable within the
population.
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Herd immunity Cont’d
Diphtheria R0= (P.C.D) Herd immunity needed to stop
transmission
Diphtheria 6-7 85%
Measles 12-18 85-95%
Mumps 4-7 80-85%
Pertussis 12-17 90-95%
Polio 5-7 80-85%
Rubella 6-7 85%
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The following table shows the herd immunity level needed to stop the
transmission of common vaccine preventable disease.

Measures of Disease Outbreak
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 Attack Rate (AR): Quantifies the incidence of an illness (usually in
the form of epidemic) among population at risk in a given time.
 Secondary Attack Rate (SAR): Quantifies the spread of disease
among contacts of an index case in circumscribed group like
households and dormitories.
 Case Fatality Rate: Quantifies the proportion of number of deaths
among those who have the disease in a given time interval.
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Epidemiological Study Designs
Study design: definition
 A study design is a specific plan or protocol for
conducting the study, which allows the
investigator to translate the conceptual
hypothesis into an operational one.
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 Key messages
 Choosing the appropriate study design is a crucial
step in an epidemiological investigation.
 Each study design has strengths and weaknesses.
 Epidemiologists must consider all sources of bias and
confounding, and strive to reduce them.
 Ethical issues are important in epidemiology, as in
other sciences.
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Epidemiological Study Designs…
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Epidemiological Study Designs…
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 The basic designs in epidemiologic research are
categorized into two:
 Observational :- The investigator measures but does
not intervene. Observational studies allow nature to
take its course.
 Interventional (Experimental):- involve an active
attempt by the investigator to change a disease
determinant or the progress of a disease through
treatment.
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Case-control
Cohort
Individuals
Intervention
Retrospective
Prospective
Descriptive
Populations
Analytical
Observational
Case report/Case-series
Cross-sectional
Ecologic/correlational
Clinical trials
Epidemiological studies
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Case Report/Series
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 This is a descriptive study of a single individual
(case report) or small group (case series) in
which the possibility of an association between
an observed effect and a specific exposure is
assessed based on detailed clinical evaluations
and histories of the individual(s).
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Case report/series are usually considered:
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 An unexpected association between exposure and
disease,
 An unexpected association between diseases,
 An unexpected event in the course of observing or
treating a patient.
 Findings that shed new light on the possible
pathogenesis of a disease or an adverse effect.
 Unique or rare features of a disease.
 Unique therapeutic approaches.
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 Example a case report
A 40 years old women who developed pulmonary
embolism after beginning use oral contraceptive.
 Example of case series
Fifteen young women who developed breast cancer;
9 of whom reported at least once weekly ingestion of
foods packed with the estrogenic chemical bisphenol
A(BPA). Urine testing confirms the presence of BPA
among all nine cases.
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Case Report/Series…

Case report/series Cont’d
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Advantages of case report/series:
 Usually provides the first evidence of innovative
treatment and emergence of a disease.
 Useful when the disease is uncommon.
 Useful to generate hypothesis.
Disadvantage of case report/series:
 Report is based on single or few patients.
 No control group is involved.
 Lack of a denominator to calculate rates of disease.
 Sampling variation
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Correlational (Ecological) Studies
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 An ecological study is an epidemiological study in
which the unit of analysis is a population or groups of
people rather than an individual.
 Level of disease occurrence and exposure are
measured in each of a series of populations and
their relation is examined.
 Commonly Correlation Coefficient (r) is used as a
measure of association between disease occurrence
and exposure.
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Correlational Studies Cont’d
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The populations compared may be defined in various ways.
1. Geographical comparison: Look for geographical
correlations between disease incidence or mortality and
the prevalence of risk factors.
2. Time trend comparison: Look for the association
between exposure and health outcome across different
points in time.
3. Occupation and social class comparison: Look for the
association between exposure and disease outcome
across different social classes.
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Advantages of ecological studies:
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 Often the information about disease and exposure is
abstracted from published statistics and therefore
does not require expensive or time consuming data
collection.
 It helps to generate a hypothesis.
 Often appropriate approach for studying broad social
and cultural processes. (Examine rates of disease in
relation to a population-level factor)
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Disadvantages of ecological studies:
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 Exposure and disease are not linked at individual level.
 Lack of ability to control for effect of potential confounders.
 The ecological fallacy: The average value of the exposure and
outcome may not apply to all the individuals in a population.
As a result an association observed between variables on an
aggregate level does not necessarily represent the association
that exists at individual level eg. Rain fall in Canada and Maize
production in Ethiopia.
 Diagnostic criteria used in different population and time might
be different.
 Difficulty to detect non-linear association between exposure
and outcome.
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Cross-sectional Studies
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 Also known by the name survey or prevalence studies.
 It is the most commonly used epidemiologic study design.
 The typical feature of cross-sectional study is it involves
single period observation.
 Such studies are usually descriptive. Thus they focus on the
assessment of the level exposure or outcome at one point in
time.
 It is also possible to examine the relationship between a
disease and an exposure among individuals in a defined
population at a point in time.
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Cross Sectional Cont’d
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Advantage of cross sectional studies:
 Useful for hypothesis generation.
 It is less resource and time consuming.
 As it involves large sample size. it is more likely to provide
generalizeable findings.
 Repeated cross sectional studies can indicate trend in
disease or risk factor.
Disadvantage of cross sectional studies:
 Usually affected by “chicken or egg” dilemma.
 Not suitable for study of rare situations.
 Not suitable for the study of acute disease.
 Liable to “survivor bias” (obtaining data only from those who
have survived to provide it).
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Case-Control Study design
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 Subjects are selected with respect to presence or absence of out
come of interest (e.g. disease), and then inquiries are made about
past exposure to factors of interest.
 Those who have the outcome of interest are termed as “cases”,
and those who do not have the outcome of interest are termed as
“controls”.
 The exposure histories of cases and controls are then obtained
and compared.
 Thus, the central feature of case control study is the comparison
of the cases’ and controls’ exposure histories. Case control
study is usually retrospective in nature.
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Case-Control Cont’d
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Advantage of case control study:
 Optimal for the evaluation of rare disease.
 Suitable for diseases with long induction period.
 Can examine multiple etiologic factors for a single
disease.
 Quick and inexpensive in comparison with cohort study.
Disadvantage of case control study:
 Inefficient for the evaluation of rare exposures.
 It is restricted to single outcome.
 Liable to “recall bias”
 Liable to “survivor bias”
 Liable to “selection bias”
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Case-Control cont’d
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Selection of Cases:
 The starting point of most case-control studies is the
identification of cases.
 This requires a suitable case definition. A case
definition is usually based on a combination of signs
and symptoms, physical and pathological
examinations, and results of diagnostic tests.
 It is best to use all available evidence to define with
as much accuracy as possible the true cases of the
disease.
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Selection of controls:
 Controls are those who have not developed the
outcome of interest and are a sample of the population
that produced the cases.
1. Controls must come from the same base population as the
cases (the control resemble the case except in outcome
status),
2. Comparability of controls with cases, (comparability is
more important than representativeness)
3. Controls must be sampled independently of exposure
status (exposed and unexposed controls should have the
same probability of selection)
4. Outcome ascertainment criteria should be the same for
cases and controls. February 2, 2024
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 Control selection is usually achieved through
matching.
 Matching is the process of selecting controls so that
they are similar to the cases in regard to certain
characteristics.
 Hence, we should identify matching variables (e.g.
age), and develop matching criteria (e.g. control
must be within the same 5 year age group) in
advance.
 Here it is important to note that controls can be
individually matched or frequency matched.
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 Individual matching: For a case search for one (or more)
controls who have the required matching criteria.
 Frequency (group) matching: Select a population of controls
such that the overall characteristics of the group match the
overall characteristics of the cases (i.e. based on matching
variables).
 During the matching process it is important to avoid “Over
matching” i.e. match only on factors known to be causes of the
outcome of interest.
 The power of study can be increased by matching a case with
multiple (usually <4) controls. There is no further gain of
power above four controls per case.
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Epidemiologists use several sources for identifying
controls in case control studies. They may sample:
 Individuals from the general population
 Individuals attending a hospital or clinic
 Friends or relatives identified by the cases
 Rarely individuals who have died.
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Population control:
Population controls are typically selected when cases are identified
from a well defined population such as residents of a geographic
area. These controls are usually identified using available
registration system.
Advantage of population controls:
 They are more likely to be comparable to the cases with respect
to socio-demographic and other important variables.
Disadvantage of population controls:
 Time consuming and expensive to identify them.
 Do not have the same level of interest in participating as do
cases,
 Because they are generally healthy, their recall may be less
accurate than that of cases
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Hospital/clinic controls:
 The guiding principles in selection of controls from
health institutions are:
1. The illnesses in the control group should be
unrelated to the exposure under study.
2. The controls should be comparable with
cases (type of hospital, severity of illness etc).
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Advantage of hospital/clinic controls:
 They are easy to identify and are less
expensive
 Usually they have good participation rates,
 Their recall of prior exposures will be similar to
the cases’ recall, because they are also ill.
Disadvantage of hospital/clinic controls:
 Difficulty in determining appropriate illness for
inclusion.
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Special Controls:
 In rare circumstances, “special” controls can be
considered.
 A friend, spouse, or relative (usually a sibling)
can be nominated by a case to serve as his or
her control.
 These “special” controls are used because
they are likely to share the cases’
socioeconomic status, race, age, educational
level, and genetic characteristics, if they are
related to the cases. February 2, 2024
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Deceased controls:
 Deceased controls are occasionally used when some or
all of the cases are deceased by the time data collection
begins.
 Researchers usually identify these controls by reviewing
death records of individuals who lived in the same
geographic area and died during the same time period as
the cases.
 The main rationale for selecting dead controls is to ensure
comparable data collection procedures between the two
groups.
 However, many epidemiologists discourage the use of
dead controls because these controls may not be a
representative sample of the source population that
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Ascertainment of exposure
75
 Ascertainment of exposure should attempt to obtain sufficiently
detailed information on the nature, frequency, and duration of
these exposures.
 Sources available for obtaining exposure data include:-
 in-person interviews,
 self-administered questionnaires,
 preexisting medical registry,
 employment record,
 environmental records,
 physical examination and
 biological specimens
 Imaging
 Accuracy of exposure data is a particular concern in case
control studies as it is collected retrospectively. February 2, 2024
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Major findings revealed by case control studies:
 Few of the major scientific association discovered by
case control studies include:
Cigarette smoking and lung cancer,
Post-menopausal estrogens exposure and
endometrial cancer,
Aspirin and Reyes syndrome,
Tampon use and toxic shock syndrome,
AIDS and sexual practices,
Assessment of different vaccines
effectiveness.
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Design of a Case-Control Study cont’d
77
Not
Exposed Exposed
Not
Exposed
Disease No Disease
“CASES” “CONTROLS”
Exposed
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FIRST: Select
CASES CONTROLS
(With Disease) (Without Disease)
THEN: Were exposed a b
Measure
Exposure Were not exposed c d
TOTALS a + c b + d
Proportions a b
Exposed a + c b + d
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79
a
c ad
Odds Ratio = =
b bc
d
a b
c d
Case Control
E+
E-
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what is matching?
 The process of making a study group and a comparison group
comparable with respect to extraneous factors.
 In case-control studies, we match to make cases and controls as
similar as possible with regard to potentially important
confounding factors.
 Matching addresses issues of confounding in the DESIGN
stage of a study as opposed to the analysis phase
 A means of providing a more efficient stratified analysis
rather than a direct means of preventing confounding, by
increasing precision of estimates (reduction in SE)
Matching in Case-Control Designs
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Types of matching
1. Individual (paired) matching: for each case, one
(or more) controls with the relevant characteristics
matching the case are chosen
 For continuous variables such as age or weight,
controls may be selected if they are within a
specified range of the control value
 Example: Age ± 2 years
 Example: Weight ± 5 pounds
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 When matching on a variety of characteristics,
including continuous variables, it may be very difficult to
individually match on all characteristics
 Minimum Euclidean distance: identify the individual
who is the closest match with regard to all of the
variables.
 We usually do this using mathematical modeling
techniques
Types of matching…
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2. Frequency (Group) matching: Controls are selected
such that the distribution of the relevant characteristic in
the controls is similar to the distribution in the cases
Ex. 1
 If 30% of cases are smokers, then select a control group
in such a way that 30% of controls are smokers
Ex. 2
 Frequency matching on age and sex. If 20% of cases
are 50-54 year old females, then controls are selected in
such a way that 20% are also 50-54 years old and
females. February 2, 2024

Advantages of Matching
 May be the best way to control for a strong confounder
when there is little overlap of the confounder between
the cases and controls
 Example: If the cases tend to be older (CHD, prostate cancer)
and a random sample of controls would result in a much
younger control group, then there may not be much overlap of
age between cases and controls
 This lack of overlap makes adjustment for confounding difficult.
Why?
 When the confounder is strong, matching increases the
efficiency of the study (by decreasing the width of the
confidence intervals around an estimate)
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 Matching can be a useful method of sampling
controls when cases and controls are identified from
a reference population for which there is no available
sampling frame (list).
 Example: Reference population is patients at a clinic or
hospital
Advantages of Matching…
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Disadvantages of Matching
 It may be difficult (and expensive) to identify a
matched control
 When you match on a characteristic, you create an
equal distribution in the cases and controls.
Therefore, you cannot examine the association
between the matched characteristic and the outcome
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Disadvantages of Matching…
 You cannot assess additive interaction between the
matching variable and the exposure of interest
 You must account for matching in the data analysis
 You may create groups that are no longer
representative of the reference population, thus
decreasing your ability to generalize your findings
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Disadvantages of Matching…
 If you match on a characteristic that is a weak confounder,
you may decrease the statistical power of your study
 If you match on a characteristic that is strongly correlated
with the exposure of interest, you may overmatch
 If you categorize continuous variables too broadly, you
may still have residual confounding
N.B.
 Sample size in unmatched study is number of cases
and controls
 Sample size in matched study is number of matched
pairs (or triplets, etc).
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Overmatching
 Overmatching occurs when you match on a variable
that is strongly correlated with the exposure of interest
 By setting the distribution of the matching variable to
be equal between cases and controls, you are
effectively setting the distribution of the exposure
variable to be equal between cases and controls
 In doing so, you will be unable to detect a difference
in exposure between cases and controls
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Residual confounding
 Occurs when you categorize continuous variables
 Ex. Create age categories for matching
20-25
25-30
30-35
For each case between 20 and 25, select a control
who is also between 20 and 25
 Now your cases and controls are comparable with
respect to age right?
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Residual confounding…
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Analysis of matched data
 Rationale is to control at the design stage for potential
confounders
 Unit of Analysis is the matched case-control pair
 Mantel-Haenszel OR
 If we pair-match cases and controls, we keep them in
pairs for the calculation of the odds ratio
 What combinations will be possible with regard to
exposure?
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Analysis of matched data…
 Concordant pairs:
 Both case and control are exposed
 Neither case nor control are exposed
 Discordant pairs
 Case is exposed, control is not
 Control is exposed, case is not
 What do the concordant pairs tell us?
 Nothing
 We are interested in the discordant pairs
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95
How do you interpret the MH OR? Just as usual.
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96 February 2, 2024
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Multivariate analysis of matched data
 Conditional logistic regression
 Use when you have individual matching
 Analogous to logistic regression, but the model
takes into account the pairing of cases and controls
 Logistic regression
 Use when you have frequency (group) matching
 Simply include the matching variables in the model
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Cohort Study designs
98
 The term “cohort” means group of people with a
common characteristic or experience.
 Cohort study is a design in which two groups are
defined according to their exposure status (exposed
and unexposed groups) and the rate of occurrence
of the interest of outcome is compared between the
two groups to explore association between the
exposure and outcome.
 Cohort study is also known as follow up study or
incidence study or longitudinal studies.
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Cohort Study design cont’d
99
There are two types of cohort studies:
 Prospective and Retrospective Cohort Studies.
 The classification is based on the temporal relationship
between the initiation of the study and occurrence of the
disease.
 In prospective cohort study, the outcome of interest has
not happen at the beginning of the study.
 However in retrospective cohort study both exposure and
outcome statuses have occurred at the beginning of the
study.
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 In comparison, retrospective cohort is cheaper, faster
and efficient for diseases with long latent period.
 However, data on ascertainment of exposure and
information on confounding factors might be missing. It is
also liable to different biases.
 Prospective cohort study is more expensive and time
consuming. It is not efficient for diseases with long latent
periods.
 However, it has better exposure and confounder
data. It is also less vulnerable to biases.
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 There are two types of cohort that epidemiologists
follow:-
 A closed cohort is one with a fixed membership.
Once it is defined and follow-up begins, no one can
be added to a closed cohort. However, as people in
the cohort die, are lost to follow-up, or develop the
disease they are excluded from follow-up.
 In contrast, an open cohort, which is also referred
to as a dynamic cohort, can take on new members
as time passes.
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 In cohort studies, epidemiologists usually calculate
incidence rates (by dividing the number of new
events in the follow up period by the appropriate
denominator, based on the size of the population at
risk).
 Conclusion is reached by comparing the incidence
rates in the two exposure groups.
RR= Incidence in exposed
Incidence in non-exposed
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The major considerations during cohort study are:
103
 Cohorts must be free from disease at the beginning of the study,
 Exposure status definition should consider dose, frequency and
duration of exposure,
 Both groups should be equally susceptible to disease under study,
 Both groups should be reasonably comparable in respect of all
possible variables which may influence the frequency of the
disease,
 The observation must take place over a meaningful period of time,
 Loss to follow-up should be as minimum as possible,
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Advantage of cohort study: (mainly for the prospective
type)
104
 Helps to understand the natural history of a disease,
 Provide clear temporal relation between exposure and outcome.
 Since information is collected longitudinally (prospective cohort) it is more
valid.
 Optimal for the study of rare exposures,
 Can examine multiple effects of a single exposure (multiple outcomes),
 Minimize bias in ascertainment of exposure.
 Cohort study also gives birth to case-control studies, using incident cases
(nested case control study).
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Disadvantage of Cohort Study: (mainly for the
prospective type)
105
 Inefficient in the evaluation of rare disease
 Can’t study the effect of multiple exposures at a time
 Inefficient in the evaluation of disease with long
latent period
 Expensive and time consuming.
 Loss to follow-up.
 The exposure might not be “fixed exposure”.
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Selection of Comparison Group
106
 There are three sources for the comparison group in a
cohort study:
1. An internal comparison group
2. The general population
3. A comparison cohort.
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107
1. An internal comparison group consists of
unexposed members of the same cohort.
 An internal comparison group should be used whenever
possible, because its characteristics will be most similar
to the exposed group. E.g. Premature births with other
normal births of the same cohort. Other example is
obesity and myocardial infarction. Subjects from the
cohort were categorized into one of five levels of body
mass index, and the group with the lowest BMI was used
as an internal comparison group or reference group,
against which the other categories were compared.
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108
2. The general population is used for comparison when it
is not possible to find a comparable internal comparison
group. This is used occasionally in situations where only
a small percentage of the population is exposed, e.g.,
with an unusual occupational exposure. However, the
general population may differ from the exposed work
force in many ways, including overal health.
3. A comparison cohort consists of members of another
cohort. It is the least desirable option because the
comparison cohort, while not exposed to the exposure
under study, it can potentially be exposed to other
potentially harmful substances and so the results can be
biased.
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109
Some of the prominent cohort studies:
 Association between employment in nickel refinery factories and lung
cancer (1929-1938),
 Cohort study on British doctors to assess the association between
smoking and lung cancer (1954-1980’s)
 Framingham study (1947, High density lipoprotein as a protective factor
against coronary heart disease:: The Framingham study till date)
 Association between employment in coal gas manufactures and lung
cancer (1950’s),
 Exposure to ionizing radiation and the incidence of leukemia (1951-1991)
 Lifespan study of the survivors of atomic bomb explosion in Japan
(1956-till date),
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Follow-up
110
 In prospective cohort study, follow up is usually made
through periodical medical examination, reviewing of
physician records, routine, death records, interviews etc.
 Loss to follow-up occurs either when the participant no
longer wishes to take part or when he or she cannot be
located. Because high rates of follow-up are critical to the
success of a cohort study, investigators should
developed methods to maximize retention and trace
study participants.
 Especially when loss to follow-up is not comparable
between the two groups, bias can be introduced. It is
usually recommended to keep the loss to follow up below
20%. February 2, 2024
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111 February 2, 2024
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A nested case control (NCC) study and
A case-cohort study
 A nested case control (NCC) study is a variant of
a case-control study in which only a subset of controls
from the cohort are compared to the incident cases
(Matching).
 The nested case-control design is particularly
advantageous for studies of biologic precursors
of disease
 In a case-cohort study, all incident cases in the cohort
are compared to a random subset of participants who
do not develop the disease of interest (No matching)
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A nested case control…
 In contrast, in a nested-case-control study, some
number of controls are selected for each case from
that case's matched risk set.
 By matching on factors such as age and selecting
controls from relevant risk sets; the nested case
control model is generally more efficient than a case-
cohort design with the same number of selected
controls.
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 Usually, the exposure of interest is only measured among
the cases and the selected controls. Thus, the nested case
control study is less efficient than the full cohort design.
 The NCC design is often used when the exposure of interest
is difficult or expensive to obtain and when the outcome is
rare.
 By utilizing data previously collected from a large cohort
study, the time and cost of beginning a new case-control
study is avoided.
 By only measuring the covariate/predictor/ in as many
participants as necessary, the cost and effort of exposure
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118
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Example of Nested case control study
 Suppose a prospective cohort study were conducted among almost
90,000 women for the purpose of studying the determinants of cancer
and cardiovascular disease. After enrollment, the women provide
baseline information on a host of exposures, and they also provide
baseline blood and urine samples that are frozen for possible future use.
The women are then followed, and, after about eight years, the
investigators want to test the hypothesis that past exposure to pesticides
such as DDT is a risk factor for breast cancer. Eight years have passed
since the beginning of the study, and 1,439 women in the cohort have
developed breast cancer. Since they froze blood samples at baseline,
they have the option of analyzing all of the blood samples in order to
ascertain exposure to DDT at the beginning of the study before any
cancers occurred. The problem is that there are almost 90,000 women
and it would cost $20 to analyze each of the blood samples. If the
investigators could have analyzed all 90,000 samples this is what they
would have found the results at the right.
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120
Example of Nested case control study…
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 If they had been able to afford analyzing all of the
baseline blood specimens in order to categorize the
women as having had DDT exposure or not, they would
have found a Risk Ratio = 1.87 (95% confidence
interval: 1.66-2.10).
 The problem is that this would have cost almost $1.8
million, and the investigators did not have the funding
to do this.
121
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 While 1,439 breast cancers is a disturbing number, it is
only 1.6% of the entire cohort, so the outcome is
relatively rare, and it is costing a lot of money to
analyze the blood specimens obtained from all of the
non-diseased women.
 There is, however, another more efficient alternative,
i.e., to use a case-control sampling strategy. One could
analyze all of the blood samples from women who had
developed breast cancer, but, only a sample of the
whole cohort in order to estimate the exposure
distribution in the population that produced the cases.
122
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 If one were to analyze the blood samples of 2,878 of
the non-diseased women (twice as many as the
number of cases), one would obtain results that
would look something like those in the following
figure:-
123
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124
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125
February 2, 2024
Case-Cohort study design
 In April of 1986, Ross Prentice published a paper in Biometrika
introducing the case-cohort design.
 This innovative design uses a sub-sampling technique in survival
data for estimating the relative risk of disease in a cohort study
without collecting data from the entire cohort.
 A case-cohort study is similar to a nested case-control study
in that the cases and non-cases are within a parent cohort;
cases and non-cases are identified at time t1, after baseline.
 In a case-cohort study, the cohort members were assessed
for risk factors at any time prior to t1. Non-cases are
randomly selected from the parent cohort, forming a sub-
cohort. No matching is performed.
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Advantages of Case-Cohort Study:
 Similar to nested case-control study design:
 Efficient– not all members of parent cohort require
diagnostic testing
 Flexible– allows testing hypotheses not anticipated
when the cohort was drawn (t0)
 Reduces selection bias – cases and non-cases
sampled from same population
 Reduced information bias – risk factor exposure can
be assessed with investigator blind to case status
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Other advantages, as compared to nested
case-control study design:
 The sub-cohort can be used to study multiple
outcomes
 Risk can be measured at any time up to t1 (e.g.
elapsed time from a variable event, such as
menopause, birth)
 Sub-cohort can be used to calculate person-time
risk
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Disadvantages of Case-Cohort Study as
compared to nested case-control study design
 Increased potential for information bias
because
 Sub-cohort may have been established after
t0
 exposure information collected at different
times (e.g. potential for sample
deterioration)
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Statistical Analysis for Case-Cohort
Study:
 Risk Ratio (RR)
 Weighted Cox proportional hazards regression
model
HR (Hazard Ratio)
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Interventional (Experimental) Study
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Interventional (Experimental) Study…
132
 An experimental study, also known as a trial,
investigates the role of some agent in the prevention
or treatment of a disease.
 In this type of study, the investigator assigns
individuals to two or more groups that either
receive or do not receive the preventive or therapeutic
agent.
 The group that is allocated the agent under study is
generally called the treatment group, and the group
that is not allocated the agent under study is called the
comparison group. February 2, 2024
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 Depending on the purpose of the trial, the
comparison group may receive;
1. No treatment at all
2. An inactive treatment such as a
placebo
3. Active treatment.
133
Interventional (Experimental) Study…
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Interventional (Experimental) Study cont’d
134
 The active manipulation of the agent by the
investigator is the hallmark that distinguishes
experimental studies from observational ones.
 In the latter, the investigator acts as a passive
observer merely letting nature take its course.
Because experimental studies more closely
resemble controlled laboratory investigations, most
epidemiologists believe that experimental studies
produce more scientifically rigorous results than do
observational studies.
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Challenges of experimental studies:
137
 Ethical issues: Ethical considerations prevent evaluation of
many treatments or procedures using an interventional
strategy.
 Recruitment of subjects: Getting adequate subjects to be
enrolled into the study is not easy.
 Cost: In most of the cases, experimental studies are vey
expensive.
 Non compliance: When study subjects are expected to take
medication for long period of time non compliance can be a
challenge to the study.
February 2, 2024
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Classification of experimental studies:
138
 Experimental studies are commonly classified based
on multiple parameters.
 Based on the objective:
 Preventive or prophylactic trial. Example: trial
on agents such as vitamins or behavioral
modifications.
 Therapeutic or clinical trial: Treatments such
as surgery, radiation, and drugs are tested
among individuals who already have a
disease.
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139
Based on population type involved:
 Clinical trial: Usually performed in clinical
setting and the subjects are patients.
 Field trial: Usually performed in the field and
the subjects are healthy people.
 Community trial: Field trial using large unit of
measurement than individuals (e.g.
community).
Classification of experimental cont’d
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140
 Based on design:
 Uncontrolled trial: Involves no control group.
 Non randomized control trial: There is control
group but allocation into either group is not
random (Quasi experimental).
 Randomized control trial: There is control
group and allocation into either group is
randomized.
Classification of experimental cont’d
February 2, 2024
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Classification Based on level: cont’d
143
 Phase I studies(clinical pharmacologic studies)
 Test new drug or treatment in a small group of healthy
people (20–80) for the first time to evaluate its safety
 Determine levels of toxicity, metabolism, pharmacological
effect, and safe dosage range
 Identify side effects
 Phase II studies (efficacy studies)
 The drug or treatment is given to a larger group of diseased
people (100–300) for efficacy and to further evaluate its safety
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Classification Based on level: cont’d
144
 Phase III studies(effectiveness studies)
 The drug or treatment is given to a large sampled group
of diseased people (1,000–3,000) to confirm its
effectiveness, compare it to commonly used treatments,
and monitor side effects
 Phase IV studies(post-marketing clinical trials)
 The drug or treatment is monitored to gather more
information on risks, benefits, and optimal use
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Selection of study population
145
 This starts with the selection of reference and
experimental population.
 Reference population is the general group
to whom investigators expect the result of the
trial to be generalized.
 While experimental population, is the actual
population in which the trial is conducted.
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Selection of study population cont’d
146
 The experimental population is enrolled on the basis of
eligibility criteria that reflect:
 the purpose of the trial
 scientific safety
 practical considerations.
 The study population must include an adequate number of
individuals in order to determine if there is a true difference
between the treatment and comparison groups.
 An investigator determines how many subjects to include by
using formulas that take into account the anticipated
difference between the groups, the expected rate of the
outcome, and the probability of making statistical errors.
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Treatment assignment
147
 Once adequate number of subjects are identified
then they are assigned to receive one of the two or
more treatments being compared.
 Randomization, an act of assigning subjects to
different treatment categories based on a random
process, is the best option since it is less prone to
bias.
 Random assignment methods include flipping a coin,
using a random number table and using a
computerized random number generator.
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Treatment assignment cont’d
148
 Randomization is important component of clinical
trial because:
 It avoids selection bias,
 It increases comparability between
different treatment groups,
 Treatment groups will not be known to
the researcher.
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Treatment administration
149
 In the next phase of a trial, the treatments are
administered according to a specific protocol. For
example, in a therapeutic trial participants may be
asked to take either an active drug or an inactive drug
known as a placebo. The purpose of placebo is:
 To mask the subjects about the treatment category
they are in (to avoid “participant effect”).
 To mask the researcher about the treatment category
that a subject is in (to avoid “observer effect”)
 Placebo effect ????
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Treatment administration cont’d
150
 However, in clinical trials, administration of placebo
for a disease with known treatment is not ethical.
 The recommendation is the best known treatment
should be given for the control group and the new
drug (expected to be better than the best known
drug) is given for the treatment group.
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Noncompliance
151
 All experimental studies require the active involvement and
cooperation of participants.
 The failure to implement the requirements of the protocol is
known as Noncompliance.
 Noncompliance may occur in the treatment group, the
comparison group, or both. It is problematic because it can
introduce bias and compromise the power of study.
 Reasons for not complying include toxic reactions to the
treatment, lack of interest, and desire to seek other
therapies.
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Some of the measures which are used
to enhance compliance are:
152
 Enrolling motivated participants
 Presenting a realistic picture of the required tasks during the
consent process
 Designing an experimental regimen that is simple and easy
to follow
 Maintaining frequent contact with participants during the
study
 Conducting a run-in period before enrollment and
randomization
 Assessment of compliance using different techniques like
self reporting, pills count and biochemical test.
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Ascertaining the outcome
153
 During the follow-up stage of an experimental study, the treatment and
comparison groups are monitored for the outcomes under study.
 If the study’s goal is to prevent the occurrence of disease, the outcome is
usually the:
 incidence of the disease.
 If the study is investigating a new treatment among individuals who
already have a disease, the outcomes may include
 disease recurrence,
 symptom improvement,
 length of survival, or
 side effects.
 The length of follow-up can range from a few months to a few decades.
Usually, all reported outcomes under study are confirmed in order to
guarantee their accuracy.
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Attributes of unbiased interventional study
154
 The quality of “gold standard” interventional
studies can be achieved through:
 Randomization
 Controlling
 Use of placebo
 Double blinding
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Data Analysis
155
•Primary: intention to treat-
•Analyze according to original allocation
•Net effect of non-compliance is to reduce the
observed differences
•Secondary: actual treatment received
•Based on observed data
•No benefit of randomization
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Crossover designs
 Cross-over studies are when participants are randomly
allocated to one treatment for a period of time and then ‘cross-
over’ to the other treatment for the second period of time.
• In this design each patient receives ALL treatments
• Comparison of treatments is within-patient comparison
• Removes all fixed within-patient factors e.g. gender
• Essentially a matched design
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Randomization
•Order of receipt is randomised
•With 2-period, 2-treatments – AB or BA
•With 3-preriod, 3-treatments –
•ABC, ACB, BAC, BCA, CAB, CBA
•Note above are balanced in the sense that every patient
gets every treatment
•Requires a wash-out period between treatments – to
prevent CARRY-OVER EFFECT
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Crossover Trial
RANDOMISED
Eligible subjects
Intervention Control
Control Intervention
Wash-out Wash-out
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Strengths and Weaknesses of crossover design
• Within patient characteristics remain same since
matched analysis
• Smaller sample size needed compared with parallel
design - Very efficient design
• Not suitable for intervention that ‘cures’ condition
• Used for treatments of symptoms / control in chronic
conditions; pain, asthma, COPD, diabetes, hypertension,
etc.
• Fails if carry-over effect from previous period
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160
• Patient drop-out in the first period can mean there are fewer
in the second period and potentially with different
characteristics
• Analysis of only complete data or ‘Per Protocol’ is likely to be
BIASED and breaks the Intention-To-Treat (ITT) principle
• Intention-to-treat analysis (ITT) A method of analysis
for randomized trials in which all patients randomly
assigned to one of the treatments are analyzed together,
regardless of whether or not they completed or received
that treatment.
Strengths and Weaknesses of crossover design…
February 2, 2024
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 Can’t cross people over from a cancer treatment to a
placebo and vice versa.
 Even when they are feasible, there can be a problem
with ‘washout’ or contamination if the drug remains in
the patients’ system will dilute the effects.
 If there is a dramatic effect of treatment, people may refuse
to cross over to new treatment.
Strengths and Weaknesses of crossover design…
February 2, 2024

Simple Analysis
Simple analysis makes use of the within-patient
comparison:
 Paired t-test for continuous outcomes
 McNemar’s Chi-squared test (Binomial test)
for categorical data
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Summary
 Design is powerful and efficient
 Eliminates within-patient confounding
 Opportunity for head-to-head trials
 Problem of carry-over effect; test often underpowered
 Drop-outs break ITT principle and per protocol analysis
could bias results
February 2, 2024

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Choice of Design
168
 As discussed before in epidemiology there are multiple
options of study designs.
 The selection of a design is dependent on multiple
situations including:
 Research question
 Researcher skill
 Availability of time
 Availability of money
 Ethical issues
 Status of existing knowledge about the research question
 Availability of data
 Nature of the exposure and outcome under study
 Character of the target population
 Duration of the natural history of a disease
 Anticipated risk of bias.
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Strength of evidence
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Measurement in Epidemiology
172
Ratio, Proportion and Rate
 The health status of a community is assessed by the
collection, analysis and interpretation of data on level
of risk factors, sickness (morbidity), disability, death
(mortality), and utilization of health services.
 Such important factors are mathematically quantified
using ratio, proportion and rate.
February 2, 2024
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Measurement in Epidemiology cont’d
173
 A RATIO quantifies the magnitude of one occurrence
(a) in relation to another (b). It is usually given as a: b,
a/b or a to b. As a and b are independent, during
calculation one is not included in the other.
 A PROPORTION is usually presented in fraction,
decimal or percentage. Unlike ratio, numerator is the
subset of the denominator, hence the value indicates
the overall contribution of the numerator to the
denominator. It measures occurrence of an event in
relation to the underlying population at risk. Eg.
(M/M+F)
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Measurement in Epidemiology cont’d
174
 A RATE is a numeric presentation which is given in the
form of fraction.
 The numerator measures one variable while the
denominator another.
 In epidemiology, the numerator is a proportion and the
denominator is unit time. Hence it measures the
occurrence of an event in a population in a period of time.
 Operationally rate has a numerator which represents
number of events, a denominator which represents
average population at risk for event, a specific time in
which the event occur, and a multiplier which is given as
100, 1000 or 100,000.
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Types of rates
175
 Crude rate: Apply to the total population of a given
area.
 Specific rate: Apply to specific sub groups in the
population (usually by age and sex).
 Adjusted rate: Are standardized rates in order to
permit comparison of rates across different
population with different age structures.
February 2, 2024
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Measures of Disease Frequency (Morbidity Measures)
176
Measurement of morbidity is more difficult than death
because of the following reasons:
1. Sickness may not be easily recognizable as there
are carriers and asymptomatic cases,
2. Sickness may occur repeatedly in one person,
3. A person may develop several diseases at point in
time,
4. There is a possibility of misdiagnosis,
5. Health and illness are not clearly defined.
February 2, 2024
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Measures of Disease Frequency cont’d
177
Comparison of morbidity status in one population with
another is also difficult because of the following
reasons:
1. Difference in the coverage of health care service,
2. Difference in the efficiency of health information
system,
3. Difference in classification of diseases,
4. Difference in case definition,
5. Difference in the technology used to diagnose
health problems.
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178
 The most basic way of measuring disease frequency is a
simple count of affected individuals.
 The measures of disease frequency used most frequently in
epidemiology fall into two broad categories, Prevalence and
Incidence.
Incidence
 Incidence quantifies the number of new events or cases or
disease that develops in a population of individuals at risk
during a specified time interval.
 There are two specific types of incidence measures.
Cumulative incidence (incidence proportion) and Incidence
rate (Incidence density or person time rate).
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179
Cumulative incidence (incidence proportion) is
proportion of people who become diseased during
specified period of time. It provides as estimate of
the probability of risk that an individual will develop
a disease during a specified period of time.
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180
 Example:
 In the study of diabetics, 100 of the 189 diabetic men
died during the 13-year follow-up period. Calculate
the risk of death for these men.
 Numerator = 100 deaths among the diabetic men
 Denominator = 189 diabetic men
CI = (100 / 189) x 100 = 52.9 per 100.
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181
 Incidence rate is a measure of incidence that
incorporates time directly into the denominator. It is
generally calculated from a long-term cohort follow-up
study, where in enrollees are followed over time and the
occurrence of new cases of disease is documented.
 Typically, each person is observed from an established
starting time until one of the following “end points” is
reached: Onset of disease, Death, Emigration, Lost to
follow-up, or the End of the study.
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182
 Similar to the incidence proportion, the numerator of the
incidence rate is the number of new cases identified during
the period of observation.
 However, the denominator differs. The denominator is the
sum of the time each person was observed, totaled for all
persons.
 This denominator represents the total time the population
was at risk of and being watched for disease.
 Note that the assumption is 100 persons each followed for
one year is the same as 10 persons followed each for 10
years.
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183
For example in a study if 50 children were
observed for 1 year, 40 for 6 months and 20 for 3
months the total person-time of observation is 75
person years.
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The following figure shows an example of
ID calculation.
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185
 ID = Number of new cases/Total person
time observation = 2/16.5 person years =
12.1/100 person years of observation.
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Example
186
 In investigating the incidence of duodenal ulcer following
the use of a specific drug in 14 subjects, 4 subjects
started the study in Jan 1990 and all finished the study in
Dec 1999 and ten subjects joined the study in Dec1995
and finished the study in November 1996. During the
period of observation 5 people developed duodenal ulcer
while taking the drug. What is the total person-year
observation? What is the incidence rate of duodenal
ulcers after taking the drug?
 Total person year observation = (4 X 10 years) + (10 x 1
year) = 50 person-years
 Incidence rate = 5 / 50 = 10 cases per 100 person-
years February 2, 2024
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General consideration in the selection of CI
and ID
187
CI may be preferred:
 If the health event has a restricted risk period
 For ease of comprehension.
ID may be preferred:
 If the health event has an extended risk
period
 If lengths of follow-up vary
 If there is a large loss to follow-up
 If the health event can reoccur
February 2, 2024
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Prevalence
188
 Prevalence, sometimes referred to as prevalence
rate, is the proportion of persons in a population
who have a particular disease or attribute at a
specified point in time or over a specified period
of time.
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Prevalence cont’d
189
There are two types of prevalence measures, Point
prevalence and Period prevalence.
Point prevalence:- refers to the prevalence measured at a
particular point in time. It is the proportion of persons with a
particular disease or attribute on a particular date.
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Prevalence cont’d
190
Period prevalence:- refers to prevalence
measured over an interval of time. It is the
proportion of persons with a particular disease or
attribute at any time during the interval.
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Interrelationship between prevalence and
incidence
191
P= I X D
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192
Factors affecting interrelation between prevalence and
incidence
1. Virulence of the disease
2. Curability of the disease
3. Availability of early detection and treatment of the
disease
4. Competing cause of death
5. Migration of cases
Important consideration in the handling of incidence and
prevalence
1. Appropriate case definition
2. Identification of persons who are at risk of developing
outcome of interest
3. Duration of observation February 2, 2024
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Use of Incidence and Prevalence
193
 Prevalence is used to measure the occurrence of chronic
diseases.
 Prevalence is used to determine disease burden and
importance of a disease in a community.
 Incidence is generally used to measure the occurrence acutely
acquired diseases.
 Incidence is more important in the study of etiology of a
disease.
 Incidence is used if one wishes to look at effect of public health
interventions.
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Reading assignment
194
 Measures of Mortality
 Standardization of Rates
February 2, 2024
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Measures of Association in
Epidemiology
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Relative Risk (Risk Ratio)
196
The relative risk (RR) expresses the risk of developing a certain disease
in people exposed to a certain factor as compared to the risk of disease in
people not exposed to the factor. From contingency table it can be
calculated as:
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197
 For example if the RR is calculated as 3, it means
the exposed group is 3 times more likely to develop
disease in comparison with the non exposed group.
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IDR cont’d
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RR strength
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OR
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OR cont’d
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Odds Ratio Vs Relative Risk
202
 When incidences are small (i.e., the outcome under study
is rare in the population), the odds ratio closely
approximates both the risk ratio and the incidence density
ratio.
 The conventional guideline for classifying a disease as
“rare” is an incidence below 10%.
 In case-control studies without additional information, the
OR is often the only measure of association that can be
estimated.
 Also, when the outcome is rare, both have approximately
the same value. In other situations (i.e., cohort or cross-
sectional data with non-rare outcomes), the
appropriateness of the OR as an epidemiologic measure of
association has been the subject of considerable debate.
February 2, 2024
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Attributable Risk (Risk Difference)
203
 Attributable Risk (AR) measures how much of the risk is due to
(attributable to) the exposure. Mathematically it is the difference
between the disease rate in exposed persons and the rate in non
exposed.
 AR = Risk in exposed – Risk in non exposed or
 AR = Incidence rate among exposed – Incidence rate among non
exposed
 Unlike the relative risk which is dimensionless, the AR as a type of
incidence rate is expressed per a certain population size per unit
time.
 While the RR is better for quantifying strength of association, the
AR is more useful from public health point of view since it can tell
how many cases of disease in exposed people could have been
prevented by eliminating the exposure.
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Attributable Risk (Risk Difference)…
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AR cont’d
205
 Example:
 A study revealed that the incidence of TB among PLWHA is
900 per 100,000 per year. On the other hand the incidence
of TB among people free from HIV is 100 per 100,000 per
year. Calculate the attributable risk and give the
interpretation.
 AR = Incidence rate among exposed – Incidence rate among
non exposed
 AR = 900 – 100 per 100,000 per year
 AR = 800 per 100,000 per year
 i.e. in population of 100,000 similar to those studied 800
develop TB due to HIV/AIDS. The remaining 100 is due to
other exposures. February 2, 2024
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Attributable Risk Percent (AR %)
The attributable risk can be expressed as a proportion called
Attributable Risk Percent (AR%). It estimates the proportion of
disease among exposed that is attributable to the exposure or the
proportion of disease that could be prevented by eliminating the
exposure. Mathematically it is given as:
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Population Attributable Risk (PAR)
207
 Since AR measures the effect of eliminating the harmful exposure on the
exposed population, it is particularly useful for planning intervention in
settings where the whole population is exposed.
 However, in situations where the exposure affects part of the population
only, it is important to know the effect of eliminating the exposure on the
population as a whole. Population Attributable Risk (PAR) considers not
only the actual incidence rate of the outcome but also the prevalence
rate of the exposure.
 PAR = AR x prevalence rate of the exposure
 For instance, in the previous example of TB and HIV, if the prevalence of
HIV is 2.3% the PAR can be calculated as:
 PAR = AR x prevalence rate of the exposure
 PAR = 800 per 100,000 per year x 2.3%
 PAR = 18.4 per 100,000 per year
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PAR cont’d
208
Population attributable risk can also be calculated as:
i.e. in a general population of 100,000 about 18.4% of TB
illness would be prevented by eliminating HIV/AIDS.
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Multiple levels of exposure
209
 Measures of association are suited to dichotomous
(i.e., two-category) measures, such as presence of
disease (yes or no) or exposure (yes or no).
 If the exposure has multiple categories measures of
association can be computed for each type or level
compared to a reference category usually the
unexposed.
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Calculation of confidence interval for OR and
RR
210
When the intention of measurement of association is to have
inference about a population parameter or to test a
hypothesis, confidence interval for OR or RR can be
calculated using the following formula.
February 2, 2024
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Causal
Inference
February 2, 2024

Applying guidelines
for
causal inference
If exposure X is
associated with
outcome Y…..then how
do we decide if X is a
cause of Y
If exposure X is
associated with
outcome Y…..then how
do we decide if X is a
cause of Y
February 2, 2024

Causal Inference…
213
Sufficient and Necessary Causes
 Epidemiological studies seek to identify causal
relationship, the three fundamental type of causation
in order of decreasing strength are:
1. Sufficient cause
2. Necessary cause
3. Risk factor
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Causal Inference…
214
 scientists have to be able to describe the nature of that
association. In order to do so, they have developed
terminology to describe the causal relationship between
two events.
1. They say that causes are necessary, sufficient,
neither or both.
2. If one thing is a necessary cause of another, then
that means that the second thing can never happen
without the first.
3. In contrast, if something is a sufficient cause, then
every time it happens the outcome will follow.
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Causal Inference…
215
 When you say that one event causes another you
may be saying that the first event is:
 Both necessary and sufficient
 Necessary but not sufficient
 Sufficient but not necessary
 Neither necessary nor sufficient
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Causal Inference…
216
All four circumstances are types of causality that occur in the
real world. Some examples are:
 Necessary But Not Sufficient
A person must be infected with HIV before he can develop
AIDS. HIV is therefore a necessary cause of AIDS;
however, since every person with HIV does not develop
AIDS, it is not sufficient.
 Sufficient But Not Necessary
Decapitation is sufficient to cause death; however, people
can die in many other ways.
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Causal Inference…
217
 Neither Necessary Nor Sufficient
Gonorrhoea is neither necessary nor sufficient to
cause pelvic inflammatory disease, because you can
have gonorrhoea without ever developing PID and PID
without ever having been infected with gonorrhoea.
 Both Necessary And Sufficient
A gene mutation associated with Tay-Sachs (is a
rare autosomal recessive genetic disorder) is both
necessary and sufficient for the development of the
disease, since everyone with the mutation will eventually
develop Tay-Sachs and no one without the mutation will
ever have it.
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Causal Inference cont’d
218
 A sufficient cause precedes a disease and has the
following relationship with it:
 if the cause is present the disease will always occur.
Examples in which this preposition holds true are rare apart
from some genetic abnormalities.
 Cigarette smoking is not a sufficient cause of lung cancer
because many people who smoke do not acquire lung cancer.
 A necessary cause precedes a disease and has the
following relationship with it: the cause must be present for the
disease to occur. However the necessary cause might be
present without the disease occurring. For example in the
absence of the organism mycobacterium, the disease
tuberculosis can not occur.
February 2, 2024
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Causal Inference cont’d
219
 A risk factor is a characteristic that if present and
active clearly increases the probability of particular
disease in a group who have the risk factor compared
with a group who don’t have.
 N.B. A risk factor is neither a sufficient nor a necessary
cause of a given disease.
 Cigarette smoking is a risk factor of lung cancer since
it increases risk of the disease.
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Component Causes and Causal Pies
220
 One of the most prominent theory of disease causation
is the epidemiological triad. However this model did not
work well for many non-infectious diseases. As a result
several other models that attempt to account for the
multi-factorial nature of causation have been proposed.
 One such model was proposed by Rothman in 1976,
and has come to be known as the Causal Pies.
 This model is illustrated in the following figure. An
individual factor that contributes to cause disease is
shown as a piece of a pie.
February 2, 2024
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Component Causes and Causal Pies cont’d
221
 After all the pieces of a pie fall into place, the pie is
complete and disease occurs. The individual factors are
called component causes.
 The complete pie, which might be considered a causal
pathway, is called a sufficient cause.
 A disease may have more than one sufficient cause,
with each sufficient cause being composed of several
component causes that may or may not overlap.
 A component that appears in every pie or pathway is
called a necessary cause, because without it, disease
does not occur. February 2, 2024
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Component Causes and Causal
Pies…N.B.”A” is the necessary cause
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cont’d
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Factors in Causation
228
 Four types of factors play a part in the causation of disease, all may be
necessary but they are rarely sufficient to cause a particular disease or
state:
1. Predisposing factors, such as age, sex, or specific genetic traits that
may result in a poorly functioning immune system or slow metabolism
of a toxic chemical. Previous illness may also create a state of
susceptibility to a disease agent.
2. Enabling (or disabling) factors such as low income, poor nutrition,
bad housing and inadequate medical care may favor the development
of disease. Conversely, circumstances that assist in recovery from
illness or in the maintenance of good health could also be called
enabling factors.
The social and economic determinants of health are just as
important as the precipitating factors in designing prevention
approaches.
February 2, 2024
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Factors in Causation cont’d
229
3. Precipitating factors /inducing/ such as
exposure to a specific disease agent may be
associated with the onset of a disease.
4. Reinforcing factors such as repeated
exposure, environmental conditions and unduly
hard work may aggravate or maintain an
established disease or injury.
February 2, 2024
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Statistical and Causal Inferences
230
 Statistical inference is not the same as causal
inference, though there is a parallelism in the
inferential process itself, and statistical inference is
generally employed in evaluating the data for use in
causal inference.
 Statistical inference may not necessarily assure causal
inference as statistical association could be emanated
from:
 some peculiarities of the study group
 problems with the measurement of disease or
exposures
 the effects of some other factor that might affect both
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 Even the “risk factor” may have occurred after (even
as a result of) the disease.
 In causal inference, one examines the structure and
results of many investigations in an attempt to
assess and, if possible, eliminate all possible non-
causal reasons for observed associations.
231
Statistical and Causal Inferences Cont’d
February 2, 2024
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Statistical and Causal Inferences…
232
 However, validity of the statistical association is an
important prerequisite for causal inference. A genuine
statistical association indicates the unlikelihood of
findings based on:
1. Chance
2. Bias
3. Confounding
 Any evaluation of evidence of causation must exclude
these three possible explanations of cause-effect
relation before proceeding any further.
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Chance
233
 Chance is the possibility that the apparent association
between a certain exposure and an outcome is based on
the particular sample studied. Random error can work to
falsely produce an association (type 1 error).
 In statistical significance testing, test statistics like z statistic,
t statistic, chi-square test are employed. All test statistics
lead to a probability statement or P value which indicate the
likelihood of obtaining a result at least as extreme as that
observed by chance, assuming the null hypothesis to be
true.
 If the p value is small (e.g. p < 0.05), the association is
referred to as being statistically significant i.e. unlikely that
the link is due to chance. February 2, 2024
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Measures to control the role of chance
234
 The larger the number of subjects of a study, the less
likely that chance to explain the association.
 In addition the larger the sample size, the easier it
will be to detect a true difference.
 Looking for consistency of the association across
studies is also one of the controlling mechanisms
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Bias
235
 Whereas chance is caused by random variation, bias is caused by
systematic variation. Bias is defined as any systematic error in an
epidemiological study that results in incorrect estimation of the
association between exposure and risk of a disease.
 Bias can occur during the process of collection, analysis and
interpretation of data. In the process of research systematic errors
create more serious problem because:
 Bias may be present without the investigator being aware.
 Influence of bias is difficult to assess.
 Based on the direction of effect bias can be categorized into:
1. Bias towards the null: observed value is closer to 1.0 than is the
true value.
2. Bias away from the null: observed value is farther from 1.0 than is
the true value.
February 2, 2024
Temesgen Gebeyehu

In general there are two types of Bias:
Selection and Observational/information bias.
236
1. Selection Bias: Refers to any error that occurs in the process of
identifying the study population. The common element of such biases is
that the relation between exposure and disease is different for those
who participate and those who should be theoretically eligible for study,
including those who do not participate.
a. Volunteer (Compliance) bias: Those who agree to be in a study
may be in some way different from those who refuse to
participate. Also known as Nonresponse (Non partcpiation) bias.
b. Loss to follow-up bias: Those that are lost to follow-up or who
withdraw from the study may be different from those who are
followed for the entire study.
c. Sampling bias: When non random samplng method is employed,
the interpretation of the study might be influenced in one way or
another.
d. Prevalence-incidence bias: This happens when prevalent cases
are used to study exposure-disease relation. This might introduce
systemic error as prevalent cases represent survivors of the
disease.
February 2, 2024
Temesgen Gebeyehu

237
2. Observational or information bias: Includes any systematic
error in the measurement of information on exposure and
outcome. Some of the common observational biases are:
a. Interviewer bias (Observer bias): This is systematic error
occurring because an interviewer (observer) does not
gather information in a similar manner in groups being
compared.
b. Recall bias: A systematic error resulting from differences
in the accuracy or completeness of recall of past events
between groups.
c. Reporting bias: A systematic error resulting from the
tendency of people in one group to be more or less likely
to report information than others. Cases with certain
diseases might be more likely to deny that they had used
alcohol than controls.
Types of Bias…
February 2, 2024
Temesgen Gebeyehu

238
d. Surveillance bias: The group with the known exposure
or outcome may be followed more closely or longer
than the comparison group.
e. Misclassification bias: Errors are made in classifying
either disease or exposure status.
f. Social desirability bias: Study partcipants may respond
in a socially desirable way.
g. Placebo effect: In experimental studies which are
placebo controlled, observed changes may be
ascribed to the possitive effect of the subject’s belief
that the intervention will be beneficial.
Types of Bias…
February 2, 2024
Temesgen Gebeyehu

Chance Vs Bias
239
 Chance is caused by random error but Bias is
caused by systematic error.
 Errors from chance will cancel each other out in the
long run (large sample size) but errors from bias will
not cancel each other out whatever the sample size.
 Chance leads to imprecise results but Bias leads to
wrong results.
February 2, 2024
Temesgen Gebeyehu

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