University of Ha'il
Faculty of Public Health
Foundation of Public
Health
Lecture No.: 3
Introduction to
Epidemiology
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The classical definition of Greek origin
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Epi –upon
Domos – the people
Ology – the study of
“the study of epidemics”
Definition of Epidemiology
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Study of the distribution and determinants of diseases and injuries in human
populations
Concerned with frequencies and types of injuries and illness in groups of people
Focus is not on the individual
Concerned with factors that influence the distribution of illness and injuries
Background
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Relatively new science – emerged in 19th century in strictest terms – study of
epidemics
Today:
 Concerned with epidemic disease and all other forms of illness and bodily injury
o Cancer, heart disease
o HIV/AIDS
o Alcoholism, drug addiction
o Suicide
o Automobile accidents
 Concerned with HC delivery processes
 Health services research or healthcare epidemiology
 Relationship between Clinical Medicine and Epidemiology
 Focus in medicine is the individual patient
 Community replaces the individual patient in epidemiology
Seven Uses of Epidemiology
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To study the history of the health of the population
To diagnose the health of the community
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To study the working of health services
To estimate from the group experience what are individual risks
To identify syndromes
To complete the clinical picture of chronic disease
To search for causes
Fundamental Assumptions in Epidemiology
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Disease doesn’t occur at random
Disease has causal and preventive factors
Disease is not randomly distributed throughout a population
Epidemiology uses systematic approach to study the differences in disease
distribution in subgroups
Allows for study of causal and preventive factors
Components of Epidemiology
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Measure of disease frequency
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Quantification of existence or occurrence of disease
Distribution of disease - three questions
o
Who is getting disease?
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Where is disease occurring?
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When is disease occurring?
Formulation of hypotheses concerning causal and preventive factors
Determinants of disease
o
Hypothesis are tested using epidemiologic studies
Progression of Epidemiologic Reasoning
1. Suspicion that a factor may influence occurrence of disease
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Observations in clinical practice
Are HC providers seeing unexpected illness patterns in their patients?
- Examination of disease patterns
 Do subpopulations have higher or lower rates?
 Are disease rates increased in the presence of certain factors?
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Observations in laboratory research
Theoretical speculation
- What theories can be generated from existing knowledge of disease
prevention and causation models?
2. Formulation of specific hypotheses
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Based on suspicions concerning influence of a particular factor on disease
occurrence
3. Conduct study
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Hypotheses are tested to determine if statistical associations between factors
and disease occurrence exist
Study population is assembled from individuals with disease or outcome of
interest and an appropriate comparison group
Data is collected and analyzed
4. Assess validity of association
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Does the observed association really exist?
- Is the association valid?
- Are there alternative explanations for the association?
 Chance
 Bias
 Confounding
5. Make a judgment of whether a cause-effect relation between factors (exposure)
exists
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What is the magnitude of the association?
Are the findings consistent with previous studies (or conflicting)?
Are the findings biologically credible?
Can underlying biological mechanisms that support the association be
identified?
Historical Perspective
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John Graunt – 1662 (Hennekins and Buring 1987)
The Nature and Political Observations Made Upon the Bills of Mortality
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Systematic statistical approach
Analyzed births and deaths in London
Excess of males born, higher mortality for males
Infant mortality is very high
Seasonal variation for mortality
Importance of routinely collected information for study of human illness
William Farr - 1839
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Examined mortality and occupation and marital status
Identified important issues in epidemiological investigations
Use of comparison population, influence of multiple factors on disease
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John Snow (1854) – Father of modern epidemiology
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Established modern epidemiologic
methods
Cholera epidemic in London
Plotted geographical location of all cases –
deaths from cholera
Went door to door, collecting information
on daily habits
Suspected water supply as source of
epidemic
Broad street pump closed, epidemic
stopped
Mode of investigation – “shoe leather”
Practical application of epidemiology – use
epidemiological investigation to impact a
health problem
Epidemiologist studies:
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Host characteristics:
 Biological factors: Age, sex, degree of immunity, other physical
attributes that promote resistance or susceptibility
 Behavioral factors : Habits, culture, lifestyle
Social environment
 Living conditions such as poverty, crowding
 Norms, values and attitudes
 Socially prescribed standards of living :
 Use of food and water, food handling practices
 Household and personal hygiene
Definitions
Epidemiologic Triad:
Traditional Model of Infectious Disease Causation
The epidemiologic triangle recognized three factors in the pathogenesis of disease:
1. Host
2. Agent
3. Environment
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AGENT
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Agent: must be present for an infection to occur: Microbial agents
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Characteristics of Infectious disease agents:
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Infectivity
Pathogenicity
Virulance
Toxigenicity
Resistance
Antigenicity
HOST
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After exposure: from sub-clinical infection (in apparent) to active case of the
disease.
End Result: Recovery, disability, disfigurement, death.
Ability to fight infections, comprises 2 broad categories:
 Non-specific defense mechanisms
 Disease specific defense mechanisms
ENVIRONMENT
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Domain in which the disease-causing agent may exist, survive or originate.
Acts as a reservoir or niche that fosters the survival of infectious disease agents. The
reservoir may be a part of the physical environment or may reside in animals or
insects (vectors) or other human beings (human reservoir – host)
External Environment: physical, biologic, social, economic components
Means of Transmission – Directly or indirectly from reservoir
DIRECT TRANSMISSION:
Spread of infection through person to person contact
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Portals of exit: sites where infectious agents may leave the body (respiratory
passages, the alimentary canal, the openings in the genitourinary system, and skin
lesions. Also through insect bites, the drawing of blood, surgical procedures and
accidents)
Portal of entry: respiratory system (influenza, cold), the mouth & digestive system
(hepatitis A), mucous membranes or wounds in the skin.
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INDIRECT TRANSMISSION:
Through an intermediary source
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Vehicle: contaminated H2O, infected blood on used hypodermic needles.
Fomites: in animated objects: doorknob or clothing.
Vectors: animate, living insect or animal that is involved with transmission of the disease
agent.
Case
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Episode of disorder, illness, or injury affecting an individual
 Case of measles
 Cancer case
 TB case
 Food poisoning event
Various sources provide case information
 Interviews or surveys
 Medical providers
 Institutions or agencies
Incidence
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Measure of new cases of disease (or other events of interest) that develop in a
population during a specified period of time
E.g. Annual incidence, five-year incidence
 Measure of the probability that unaffected persons will develop the disease
 Used when examining an outbreak of a health problem
Prevalence
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Number of existing cases of disease or other condition
o Proportion of individuals in a population with disease or condition at a specific
point of time
 Diabetes prevalence, smoking prevalence
o Provides estimate of the probability or risk that one will be affected at a point in
time
o Provides an idea of how severe a problem may be – measures overall extent
o Useful for planning health services (facilities, staff)
Epidemic, Endemic and Pandemic
Epidemic
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Any significant increase in the number of persons affected by a disease
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The first occurrence of a new disease
Endemic
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A disease that is established within a population that remain at a fairly stable prevalence
Pandemic
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Widespread, universal disease penetration over a wide geographic area
More Terms
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Morbidity n
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Mortality
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Acute disease
illnesses, symptoms, impairments
deaths
diseases that strike and disappear quickly, within a month or so
(chicken pox, colds)
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Chronic disease
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Birth cohort :
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Life expectancy: :
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long term or lifelong diseases, incurable
Persons born in a given year
Average number of years of life remaining to a person at a particular age
Based on mortality rates and personal characteristics (e.g. gender, race)
 Years of potential life lost
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Measure of premature mortality
Death before age 75
Epidemiologic Measures
RATIO
 Used to compare two quantities ( 1:1.1 ratio of female to male births )
 Used to show quantity of disease in a population
Cases
=
Populatio
n
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Proportion
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A specific type of ratio in which the numerator is included in the denominator,
usually presented as a percentage
Calculation of proportion:
E.g.
Males undergoing bypass surgery at Hospital A
Total patients undergoing bypass surgery at Hospital A
352 males undergoing bypass surgery
539 total patients undergoing bypass surgery
= 65.3%
Rate
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Special form of proportion that includes a specification of time
Most commonly used in epidemiology because it most clearly expresses probability or
risk of disease or other events in a defined population over a specified period of time
3 major types
 Crude rates
 Specific rates (age-specific, infant mortality)
 Adjusted rates
Crude rates
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Unadjusted, simple ratios
Cases in defined period of time
x K
Population in defined period of time
(K denotes units 100’s, 1,000, etc.)
E.g. Crude mortality rate:
Total deaths in 2003
x 1,000
= death rate
Estimated U.S. pop in 2003
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Calculation of rates:
Number of events in a specified time period
Xk
Population at risk of these events in a specified time period
(K is used to denote the units of population such as per 1,000 or per 100,000)
E.g.
9,981 deaths in Detroit in 2000
951,270 total population in Detroit 2000
=
10.49 per 1,000
=
1049 per 100,000
Specific Rates
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Capture effects of specific variables or social characteristics
 Age-specific, gender-specific, gender and race-specific
 Example – infant mortality – deaths within the 1st year of life
Total # of deaths among Persons age less than 1 year
x 1,000
Number of live births
= infant mortality rate
Adjusted or Standardized Rates
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Allow for comparison of populations with different characteristics
 Statistically constructed summary rates allow for appropriate comparisons by
taking into account differences in populations (age, gender, etc.)
Example of use: Population in Arizona is much older than population in Alaska, so it
would be inappropriate to compare mortality rates. Standardization allows for
meaningful comparisons.
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Calculating prevalence:
P=
Number of existing cases of disease at a given point in time
Total population at risk
e.g.
P=
2176 pts with asthma
31005 pts
=
=
=
.07
7 asthmatics per 100 pts
7%
Prevalence calculation:
Pediatric Asthma at DNW
P=
Number of existing cases of disease at a given point in time
Total population at risk
P=
2159 DNW pts < 19 with asthma encounter
9173 DNW pts < 19
Types of Prevalence
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Point prevalence: number of cases that exist at a given point in time
Lifetime prevalence: proportion of the population that has a history of a given disorder
at some point in time
Period prevalence: number of cases that exist in a population during a specified period
of time
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Cumulative Incidence
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The proportion of individuals who become diseased during a specified time period.
Time period can be a calendar year, 6 months, 3 years, 5 years, etc.
Formula for cumulative incidence:
CI =
Number of new cases of disease during a given time period
Total population at risk
CI =
70 new cases of breast cancer in a 5 year period
3,000 women at risk
= 0.023
= 23 cases per 1,000 women during 5 years
Incidence Rate
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Also known as incidence density
Measure of incidence that is able to handle varying observation periods
Denominator is sum of person-time at risk
Formula for incidence rate or incidence density:
ID =
Number of new cases of disease during a given time period
Total person-time at risk
ID =
70 new cases of breast cancer
13,000 women-years of observation
= 0.0054
= 5.4 cases / 1,000 women years
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