1. Basic Epidemiologic Concepts and Principles
Chapter 1
Basic Epidemiologic Concepts and Principles

2. Epidemiology… Study of something that affects a population
Study of factors that determine the occurrence & distribution of disease in a population
One of the ways in which disease, injury and clinical practice are studied

3. Classical Vs. Clinical Classical Clinical
Population oriented studies of the community origins of health problems.
Clinical
Use of research designs and statistical tools to study patient in health care setting
Main differences between clinical and classical epidemiology are the place of the investigation and the population being studied

4. Stages of Disease Pre-disease Stage: No disease present
Latent Stage: Asymptomatic; disease is in progress
Symptomatic Stage: Manifestation of disease
The way a disease progresses in the absence of medical
or public health intervention is often called the natural
history of the disease. During the
predisease stage (before the pathologic process
begins), early intervention may prevent exposure to
the agent of disease (e.g., lead, trans-fatty acids, or
microbes), preventing the disease process from starting;
this is called primary prevention. During the
latent stage (when the disease process has begun, but
is still asymptomatic), screening and appropriate treatment
may prevent progression to symptomatic disease;
this is called secondary prevention. During the
symptomatic stage (when disease manifestations are
evident), intervention may slow, arrest, or reverse theprogression of disease; this is called tertiary prevention.
These concepts are discussed in more detail in
Chapter 14 and subsequent chapters.

5. VECTORS Defined: Examples:
Insects, arthropods and animals which aid in the spread of disease by themselves being a host capable of transmitting disease causing organisms to the host they are living on
Examples:
Mosquitoes = West Nile Virus
Tick = Lyme Disease
Rats = Bubonic Plague
Mosquitoes = Canine Heartworm
Snail = Haemonchus contortus (nematode)
The causes of a disease often are considered in terms of a triad of factors: the host, the agent, and the environment. For many diseases, it is useful to add a fourth factor, the vector (Fig. 1-1).
Vectors of disease commonly include insects (e.g.,
mosquitoes associated with the spread of malaria), arthropods (e.g., ticks associated with Lyme disease),
and animals (e.g., raccoons associated with rabies in
the eastern US).

6. Risk Factors for Diseases
Biological/Behavioral
Environmental
Immunological (Herd Immunity)
Nutritional
Genetics (Genetic Susceptibility)
Services, Social Factors, Spiritual Factors
Herd immunity results when a vaccine reduces an
immunized person’s ability to spread a disease, leading
to reduced disease transmission.
It is well established that the genetic inheritance of
individuals interacts with diet and environment in
complex ways to promote or protect against a variety
of illnesses, including heart disease and cancer.
Genetic epidemiology is a growing field of research.
Population genetics and genetic epidemiology are concerned
with, among other things, the distribution of
normal and abnormal genes in the population and
whether or not these are in equilibrium.

7. Genetic Susceptibility
Evaluation of which of the following potentially
preventable causes of disease is most likely to
raise ethical concerns?
(A) Dietary intake
(B) Genetic susceptibility
(C) Immunization status
(D) Smoking history
(E) Social support networks
Social support networks, immunization status, dietary intake, and smoking status all are factors that can be modified to prevent disease. Currently available technology permits the identification of genetic susceptibility to some diseases (e.g., breast cancer). Because the ability to modify genetic risk is not nearly as great as the ability to recognize it, however,
ethical concerns have been raised. Little good may come from informing people about a risk that they cannot readily modify, whereas harm, such as anxiety or increased difficulty and expense involved in obtaining medical insurance, might result.

8. Epidemiologic Data Sources and Measurements
Chapter 2
Epidemiologic Data Sources and
Measurements

9. Types of Data Used in Health Studies
Numerator Data
A definition of the events or conditions of concern
Example: Lung Cancer
Denominator Data
A definition of the population at risk
Example: Smokers at risk for lung cancer
When discussing denominator and numerator data; what is the correct
configuration of these in terms of definitions?
Event/population
Population/date of event
Date of event/population
None of the above

10. Epidemiological Measurements
Frequency – deals with numbers
Incidence – number of occurrences (well: ill or live: dead)
Prevalence – number of cases within a population at a given time
Risk – deals with proportion of people unaffected at the beginning of a study
Gave us discussion between the differences of incidence and prevalence
The total number of cases of an epidemic disease
reported over time is the cumulative incidence.
According to the CDC, the cumulative incidence of
AIDS cases in the US through December 31, 1991, was
206,392, and the number known to have died was
133,232.
The introduction of the term cohort is introducted in this section. It is defined as a risk event may be death, disease, or injury,and the persons at risk for the event at the beginning ofthe study period are called a cohort.

11. Epidemiological Measurements
Rates: Frequency of events that occur in/during a defined period of time.
Generally the following rules are in play
Numerator: rate of the frequency of event
Denominator: number of people at risk during period being studied/considered
Constant multiplier: usually 100 in order to get a percentage; 1000, 10,000 or 100,000 used for numbers less than “1”. Not used a lot.

12. Methods of Data Collection
United States Census
Takes place every 10 years
Also collects continuously data on births and deaths through the Dept. of Vital Statistics
U.S Vital Statistics
Basically deals in birth and death of the population
Local and state officials collect information
Federal government collates the information after it is collected

13. Maternal and Fetal Associated Rates
Live birth is the delivery of a product of conception that shows any sign of life after complete removal from the mother.
Infant mortality rates: death of infants born alive
Neonatal and Post-neonatal Mortality Rates: death of infants a) during first 28 days of life (neo=new) and b) from 28th day to 1 year of life (post=after event).
Perinatal mortality rates: deaths that occur around the time of birth (i.e. stillborn)
Maternal mortality rates: death of a pregnant woman as a result of pregnancy related health issue
Incidence Rate
Prevalence Rate
Crude Rates
Specific Rates
Death Rates

14. Epidemiologic Surveillance & Outbreak Investigation
Chapter 3
Epidemiologic Surveillance & Outbreak Investigation

15. Types of Surveillance Passive Surveillance Active Surveillance
Routine data collection
Physicians
Clinics
Laboratories
Hospitals
Example: Infant Mortality rate in Duval County, Florida in January 2008
Active Surveillance
Periodic reports
By phone or visit
Labor intensive
Health departments
Example: The insuring that TB patients are taking their medications as directed
In passive surveillance the persons responsible for reporting data or ………
Widely publicized fatalities associated with an emerging disease such as Hantavirus may be an example of active surveillance

16. Things to Know About Outbreaks
Epidemic: Occurrence of disease at an unusual frequency
Syndromic Surveillance: Surveillance which is looking for unlikely symptoms that may identify possible bio-terrorist activity using biologicals.

17. The data in
this figure are presented in the form of a semilogarithmic
graph, with a logarithmic scale used for the
vertical, y-axis and an arithmetic scale for the horizontal,
x-axis.
Used to monitor long and secular trends

18. Elementary my dear Watson…
Investigating an Epidemic:
Establish a common diagnosis
Establish a case definition (criteria of the disease)
Establish a given number of diagnosed cases
This is somewhat predicated by the number of cases of the disease normally seen in the area at the same time of the year
Establish Time, Place and Person
index case—the case that introduced the organism into the population.

19. Causation in Epidemiologic Investigation & Research
Chapter 4
Causation in Epidemiologic Investigation & Research

20. The first and most basic requirement for a causal relationship to exist is that there must be an association between the outcome of interest (e.g., a disease or death) and the presumed cause.
Causal/Non-causal Associations: the association between the disease/death and the presumed cause of that action.
One must investigate associations in order to correctly determine that there is actually a viable cause and effect in play in terms of an epidemiological episode.

21. Pitfalls to Causal Research
There are many things that can slow or interfere with validating research into cause and effect
Bias (differential error): Most dangerous as it is a deviation or distortion of data or interpretation that goes in one direction.
Random Error: Something that happens randomly and unexpectedly for no reason, and appears as unexpected highs or lows in the statistical analysis
Confounding: Confusion of two variables in such a way as to not be able to discern which is which, causing the data to be discarded
A confounder is a third variable that is associated with the exposure variable and the outcome variable in question.

22. More Still…
Synergism: an interaction of two variables that produces an effect that is greater together then than the separate effect of the variables
Effect Modification: Appearance of an unexpected third variable which affects the performance of the two accounted for variables

23. Common Research Designs of Epidemiology
Chapter 5
Common Research Designs of Epidemiology

24. To generate a workable hypothesis
To test the hypothesis
Identify variables that may cause an effect to happen
Identify risk factors
Minimize aforementioned pitfalls from occurring (i.e. bias, confounding, etc.)

25. Research Design Generating Hypothesis
Cross sectional surveys (interviews)
Cross sectional ecological surveys (behavior)
Longitudinal ecologic studies (on-going surveillance)
Hypothesis generation is the process of
developing a list of possible candidates for the
“causes” of the disease and obtaining initial evidence
that supports one or more of these candidates.

26. Hypothesis Testing Prospective cohort studies:
The identification of a group of people/individuals on whom baseline (initial) data is collected after which the same data is collected over a long period of time
Ex: Framingham Heart Study – a study that has been on-going since 1950.
Can be expensive and time-consuming
Retrospective Cohort Study
Choosing a group of people in which something has already occurred and reviewing what their lives were like after the event.
Ex: Study of people exposed to radiation after Chenobyl incident in Russia
The defining difference between a prospective and retrospective cohort study is time

27. More Hypothesis Testing
Case-Control Studies
Randomized Controlled Clinical Trials (therapeutic in nature)
Randomized Controlled Field Trials (preventive in nature)

28. Assessment of Risk and Benefit in Epidemiologic Studies
Chapter 6
Assessment of Risk and Benefit
in Epidemiologic Studies
Epidemiologic research usually is designed to permit
one or more primary contrasts in risk, rate, or odds of
disease or exposure. The most straightforward of these
measures are the risk difference (the attributable risk)
and the rate difference, which show in absolute terms
how much the risk of one group (usually the group
that is exposed to a risk factor or a preventive factor)
differs from that of another group. This contrast also
can be expressed as a ratio of risks, rates, or odds; the
greater this ratio, the greater the difference the exposure
makes.
The impact of the risk factor on the total disease
burden can be measured in terms of an attributable
risk percent for the exposed group or for the population

29. Attributable Risk (AR)
Defined: An estimate of the amount of risk which is attributable to the risk factor
Formula:
AR = [a/(a+b)] – [c/(c+d)]

30. Relative Risk RR = [a/(a+b)]/[c/(c+d)]
Defined: This is somewhat of a comparison of the ratio of risk in an exposed group to the ratio of risk in the unexposed group.
Formula:
RR = [a/(a+b)]/[c/(c+d)]
Hint: Notice that we are dividing the two sets of numbers not subtracting them as we did with AR

31. Ratio OR = (a/c) / (b/d) Defined: An estimate of a odds ratio Formula:
HINT: Do not use the step in the book that instructs you to convert the above formula to OR = ad/bc. The reason is because the numbers sometimes become too large to work with and muddy the waters.

32. Understanding the Quality of Data in Clinical Medicine
Chapter 7
Understanding the Quality of Data in Clinical Medicine
Epidemiologic research usually is designed to permit
one or more primary contrasts in risk, rate, or odds of
disease or exposure. The most straightforward of these
measures are the risk difference (the attributable risk)
and the rate difference, which show in absolute terms
how much the risk of one group (usually the group
that is exposed to a risk factor or a preventive factor)
differs from that of another group. This contrast also
can be expressed as a ratio of risks, rates, or odds; the
greater this ratio, the greater the difference the exposure
makes.
The impact of the risk factor on the total disease
burden can be measured in terms of an attributable
risk percent for the exposed group or for the population

33. Key Concepts
Accuracy: Ability of a measurement to be correct on the average
Precision: Ability of a measurement to give the same results with repeated measurements of the same thing
Both of these are necessary in statistics and neither takes a back seat to the other
Precision Is reproducibility, reliability

34. False is False and True is True Or is it?
Type I Error
Also known as a false-positive error or Alpha error
The error is in the fact that a positive reading is registered when the results are actually negative
One way in a which a researcher can judge how useful a screening or testing procedure is involves the evaluation of the number of correct test results

35. Continued… Type II Error
Also known as a false-negative error or a beta error
The error is in the fact that a negative reading is registered when the results are actually positive

36. Sensitive Vs. Specific
Sensitivity – Ability of a test to detect the disease when present
Specificity – Ability of a test to indicate non-disease status when no disease is present
The difference between sensitivity and specificity is the presence or non presence of a disease.

37. Improving Decisions in Clinical Medicine
Chapter 8
Improving Decisions in Clinical Medicine
Epidemiologic research usually is designed to permit
one or more primary contrasts in risk, rate, or odds of
disease or exposure. The most straightforward of these
measures are the risk difference (the attributable risk)
and the rate difference, which show in absolute terms
how much the risk of one group (usually the group
that is exposed to a risk factor or a preventive factor)
differs from that of another group. This contrast also
can be expressed as a ratio of risks, rates, or odds; the
greater this ratio, the greater the difference the exposure
makes.
The impact of the risk factor on the total disease
burden can be measured in terms of an attributable
risk percent for the exposed group or for the population

38. Bayes Theorum
A somewhat convoluted theory that is used in the mathematical exercise of determining probability.
If a patient has symptom A what is the probability that the symptom is caused by a certain disease?

39. Evidence Based Medicine
A form of medical practice that:
Bases a diagnosis on symptoms + prevalence
Relies heavily on the use of Bayes Theorum
Must perform tests to rule out diseases as part of the diagnostic process
Uses a sequential approach to tests
Begin with most sensitive test for fastest and most accurate results
Continues with other tests in a sequence until a satisfactory answer is arrived at (i.e. diagnosis)

40. Decision Trees
A method of improving decision making in times of uncertainly
A way of outlining ones thoughts and rationale concerning a medical dilemma
Create a decision tree
Identify/set limits to the problem
Diagram options
Obtain information on each option
Compare the values
Perform sensitivity analysis to arrive at an answer
How many steps are included in the creating of a decision tree? 5
The decision node is a point where clinicians have to make a decision
When developing a decision tree which comes first.