1. Epidemiology Key Terms & Measures
Fran C. Wheeler, Ph.D
School of Public Health
University of South Carolina
Columbia, SC
(803)
Dr. Wheeler is the Director of the Office of Public Health Practice at the School of Public Health, University of South Carolina. Prior to joining the faculty at USC, she worked for over twenty years in chronic disease prevention and health promotion programs at the South Carolina Department of Health and Environmental Control. As a long-time consultant with the Centers for Disease Control and Prevention, she also has experience in public health policy development at the national level. Dr. Wheeler has published research on the epidemiology of chronic diseases and related risk factors, as well as community-based public health interventions to address those problems in South Carolina. Her professional interests include policy and environmental interventions for chronic disease prevention and health promotion, translating research into public health practice, and minority health.
Fran C. Wheeler, Ph.D.
Director, Office of Public Health Practice
School of Public Health
University of South Carolina
Columbia, SC 29208
Phone:
Fax:

2. Developed as part of an Enhanced AHEC Community Partnership for Health Professions Workforce and Educational Reform project funded by the Health Resource and Service Administration (HRSA)

3. OBJECTIVES epidemiology and role as foundation for public health
common measures of disease frequency
strengths and weaknesses of study designs
At the conclusion of this module, the learner will be able to:
define epidemiology and explain its role as the foundation for public health
describe three common measures of disease frequency
discuss the strengths and weaknesses of three major epidemiologic study designs
The purpose of this module is to provide an overview of the fundamentals of epidemiology as the basis for the science of public health. The module does not include details on basic statistical terms (e.g., mean, median) or basic statistical calculations (e.g. chi-square). If your students have not been introduced to these methods, it will be necessary to insert them into the module at appropriate points. On the other hand, if your students have had extensive courses in statistical methods, you may want to abbreviate this module.
It is also advisable that you identify current issues or cases most relevant to your students’ area of practice for use during this module.

4. Epidemiology
Study of distribution of determinants and antecedents of health and disease in human populations
Application of results to control of health problems
Epidemiology is the study of the distribution of determinants and antecedents of health and disease in human populations, and the application of study results to the control of health problems.

5. From Hippocrates to John Graunt
Fifth century BCE, Hippocrates pointed to the need to understand the environment and the risks it posed to understand the experience of disease
1662, John Graunt analyzed weekly reports of births and deaths in London, quantifying patterns of disease in the population
Hippocrates: “Whoever wished to investigate medicine properly should proceed thus: in the first place to consider the seasons of the year, and what effects each of them produces. Then the winds, the hot and the cold, especially such as are common to all countries, and then such as are peculiar to each locality. In the same manner, when one comes into a city to which he is a stranger, he should consider its situation, how it lies as to the winds and the rising of the sun; for its influence is not the same whether it lies to the north or the south, to the rising or to the setting sun. One should consider most attentively the waters which the inhabitants use, whether they be marshy and soft, or hard and running from elevated and rocky situations, and then if saltish and unfit for cooking; and the ground, whether it be naked and deficient in water, or wooded and well watered, and whether it lies in a hollow, confined situation, or is elevated and cold; and the mode in which the inhabitants live, and what are their pursuits, whether they are fond of drinking and eating to excess, and given to indolence, or are fond of exercise and labor.”
Graunt: Noted excess of men compared with women in births and deaths, high infant mortality rate, and seasonal variations alluded to by Hippocrates. Also attempted a numerical assessment of the impact of the plague on the population, and the characteristics of years in which such outbreaks occurred.

6. From William Farr to John Snow
200 years later, Dr. William Farr was made responsible for medical statistics in the Office of the Registrar General for England and Wales
A mere 20 years later, John Snow completed his study of cholera
Farr’s contribution is the tradition of using vital statistical data to study health problems. His studies compared married to single persons, workers in different occupations (metal mines and earthenware industry), elevations above sea level, and the effects of incarceration.
If your class did not complete Module 1a, you might want to include at this point the slides from that module on John Snow and cholera in London, which provide a dramatic illustration of epidemiologic analysis.

7. Modern Experiences
Evaluation of risk factors for chronic diseases using case controls
Long term population studies using cohorts
Design of clinical trials to evaluate interventions
Evaluation of risk factors for chronic diseases using case controls
Case control studies compare a group of people with a disease or condition to another group of people without it. The Doll and Hill (1950) study of cigarette smoking and cancer in Britain is a classic example, and is credited with starting our current series of efforts to control tobacco use.
Long term population studies using cohorts
In cohort studies, subjects are categorized on a predetermined basis and followed over time for the development of health conditions. One well-known example is the Framingham Heart Study in which 5200 residents were followed over 35 years. Findings of this study have been used to develop improved cardiovascular disease prevention methods.
Design of clinical trials to evaluate interventions
Clinical trials in humans are conducted to determine whether methods found effective in laboratory conditions can be safely applied to a large population under “normal” conditions to demonstrate its application to the control of disease.

8. Three Essential Components
Disease distribution
Disease determinants
Disease frequency
Expected level
Endemic
Sporadic
Epidemic
Pandemic
Disease distribution: how are cases of the condition of interest spread across a population differently by gender, age, geographic location, socio-economic status, other features?
Disease determinants: what risk factors or antecedent events are associated with the appearance of a disease or condition?
Disease frequency: how many cases of the condition occur over a given time period?
Expected level - baseline level of observed occurrence of a particular disease
Endemic - persistent occurrence at a low to moderate level, sometimes referred to as a high background rate
Sporadic - irregular pattern with occasional cases occurring at irregular levels
Epidemic - occurrence of a disease within an area exceeds expected level for a given time. Also called an outbreak.
Note that these mean basically the same thing, but public perspective is that epidemic is much more serious than an outbreak.
Pandemic - epidemic that has spread over several countries or continents, affecting large numbers of people

9. Epidemiologic Studies
Descriptive
Analytic
Descriptive Epidemiology provides the Who, What, When, and Where of health-related events in a population.
Analytic Epidemiology attempts to provide the Why and How of health-related events in a population.

10. Descriptive Studies Frequency of occurrence of particular condition
Patterns of occurrence according to person, place and time
In Descriptive Studies, the epidemiologist is concerned with collecting information that characterizes and summarizes a health event or health problem.
Routinely collected data from such sources as death certificates, hospital discharge records, health surveys (e.g., cross-sectional surveys) and disease surveillance programs are used for most descriptive studies.
Characteristics related to “person” may include age, gender, race, ethnicity,marital status, socioeconomic class and occupation.
Descriptive studies on occurrence of conditions according to place might involve examining their frequency within or between natural or political boundaries, urban versus rural localities, or latitude.
Examination of “time” relationships can both identify and evaluate possible causes for changes in health conditions.

11. Analytic Studies Observational studies Experimental studies
case-control studies
cohort studies
prospective
retrospective
Experimental studies
Case control studies: Case-control studies are those in which persons with a specified condition (the cases) and pesons without the condition (the controls) are selected for study. The proportion of cases and controls with certain characteristics or exposure is then measured and compared. For example, knowing that there are 10 school children with purple spots in grade 3, a set of other third grade children from the same school but without purple spots would be identified as controls, and analysis done to see what different exposures the purple-spotted children had than the non-spotted.
Cohort studies: groups of individuals with some common feature (age and geography, for example) are identified for study over time to learn about differing health and illness experiences. For example, one might enroll in a study all third graders in a school and follow them until graduation, attempting to identify the differences in experiences of those who maintained a body weight close to recommended and those who did not.
The next slides illustrate some of the differences in timing in these types of studies: case control, prospective cohort, and retrospective cohort.

12. Case Control Study Exposure Disease ? ? = present = absent Key
Investigator at beginning of study
? To be determined
At the beginning of the case control study, the investigator knows that there are some people with a disease; they are matched with similar individuals (controls) who do not have the disease. The investigator looks backward to identify what different exposures the two groups might have had. For example, when some individuals attending a picnic become ill, they could be matched with controls who also attended the picnic but did not become ill, and all interviewed about what was eaten, to identify a possible source of food-borne illness.
Key
Basis for selection of group for study
= present = absent

13. Prospective Cohort Study
Exposure
Disease ? ?
KEY
Investigator at beginning of study
? To be determined
At the beginning of a prospective cohort study, the investigator is aware of a group of individuals, some of whom have been exposed to a hazard. All members of the cohort will be followed over time to see if those exposed and those unexposed have different disease experiences.
For example, a public health department may be informed of the exposure of a portion of a school class to an individual with an active case of a communicable disease in the course of a field trip. The entire class (the cohort) would be observed over time to identify any cases of disease that arise, and any difference in disease rate between the two groups.
Key
Basis for selection of group for study
= present = absent

14. Retrospective Cohort Study
Exposure
Disease ? ?
KEY
Investigator at beginning of study
? To be determined
At the beginning of a retrospective cohort study, the investigator is aware of an exposure to a hazard that occurred at some time in the past, sufficiently long ago that if disease were to have occurred, it should by now be evident. A cohort that includes the exposed individuals is identified, and the health histories of all members explored to identify the presence or absence of disease in all individuals, and the difference in rate between exposed and non-exposed.
Many of the studies of association between environmental exposures and disease have been of this type. A cohort of individuals who lived or worked in an area but had different experiences of exposure/non-exposure to a chemical will be identified. Their health histories in the intervening years will be examined to identify differences, if any, in their rates of disease.
Key
Basis of selection of group for study
= present = absent

15. Analytic Studies Observational studies Experimental studies
Intervention studies
Clinical trials
Experimental studies generally provide the strongest evidence that a given response is the cause of a disease or that a specific intervention is effective.
The controlled clinical trial, the “gold standard” of studies of efficacy of disease interventions or prevention methods, is conducted by identifying an eligible population, dividing it into control and test groups (usually randomly), applying the intervention, and watching to see what the effect is compared to the control group.
Experimental studies typically are expensive and challenging to conduct. The methodology for these studies is extensively described in a variety of resources, and is not a major focus of this module.

16. Basic Presentation of Results
Chi-square is the basic statistic used to test for the significance of any differences noted in distribution of disease or risk in a table such as this.
A= the number of individuals who are exposed and have the disease
B= the number of individuals who are exposed and do not have the disease
C= the number of individuals who are not exposed and have the disease
D= the number of individuals who are both non-exposed and non-diseased
A+B+C+D= the total number of individuals in the population being studied
A+B= the total number of individuals exposed
C+D= the total number of individuals non-exposed
A+C= the total number of individuals with the disease
B+D= the total number of individuals without the disease
Any rates needed for epidemiologic analysis can be calculated from this basic table. Unless your students are extremely familiar with statistics, you should insert an example that will be of interest, and take some time to discuss it.
All rates and ratios discussed can be calculated from this

17. Smoking and Carcinoma of the Lung
This table shows the association between smoking and lung cancer, taken from the historic study by Doll and Hill.
Men and women with and without lung cancer were categorized as smokers or non-smokers. Thus, the exposure was smoking; the disease outcome of interest was lung cancer.
Lung cancer among exposed individuals is far in excess of lung cancer among non-exposed individuals.

18. Interpreting Results: Measurement Errors
Bias
information
selection
Confounding
extraneous factors
Effect modification
statistical interaction
Bias is the tendency of a measurement to deviate from the true value. Information bias derives from measuring the outcome or exposure such that information is more accurate or more complete in one group than another; this includes interviewer or observer bias, recall bias and reporting bias. Selection bias occurs because of differences between those who are not selected for a study or a group within a study; this includes ascertainment bias, detection bias, and response bias.
Confounding can occur when a variable related to the condition under study is associated with, but not a consequence of, the exposure under investigation. While it is never possible to control all factors in an epidemiologic study, findings will be easier to interpret if extraneous factors are eliminated or made identical for cases and controls.
Effect modification occurs when the magnitude of the association between one variable and another differs according to the level of a third variable. Detecting effect modification is an important aspect of data analysis.

19. Interpreting Results: Cause-Effect Relationship
Strength of the association
Consistency
Temporality
Plausibility
Biological gradient
In general, there are five criteria that must be met to establish a cause and effect relationship. These are:
Strength of association: Power and sample size must be sufficient to demonstrate a statistically significant difference.
Consistency: Observation of the association must be repeatable in different populations at different times.
Temporality: The cause must precede the effect.
Plausibility: The explanation must be biologically plausible.
Biological gradient: The presence of a dose-response relationship.

20. Measures of Disease Frequency
Prevalence
Incidence
Prevalence and incidence are commonly confused. They are similar, but differ in the number of cases included in the numerator:
Prevalence includes all cases during a given time period
Incidence includes only the number of new cases during a given time period.

21. Prevalence
Prevalence= number of existing cases divided by total population
Visual examination survey
X = 12.5%
2477
Prevalence 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.
The numerator for prevalence includes all persons during a specified interval or point in time, regardless of when the condition began.
For example, a visual examination survey of 2477 persons between the ages of 52 and 85 years showed that 310 had cataracts. The prevalence of the condition was
310 X = 12.5%
2477

22. Types of Prevalence Measures
This chart shows several types of prevalence rates. All have in common a numerator that includes all cases (new and old) of the condition under study.
Note: The term “period prevalence” is sometimes used to denote the number of existing cases plus new cases diagnosed during a given time period, divided by the total population.

23. Incidence
Incidence = number of new cases in a given period of time divided by the total population at risk
Bacteremia among contraceptive users
27/483 X 100 = 5.6%
Incidence is the proportion of persons in a population who develop or demonstrate a particular disease or attribute during a specified period of time.
The numerator for incidence includes only those persons who develop the condition during the specified time period.
For example, in a study of 2390 women between 16 and 49 years of age, it was found that 482 used oral contraceptives. Twenty-seven of the oral contraceptive users developed bacteremia. The incidence was therefore:
27/482 X = 5.6%

24. Types of Incidence Measures
This chart shows several types of incidence rates. All have in common a numerator that includes only new cases of the condition under study.
Note: When the denominator is the size of the population at the start of the time period, the measure is sometimes called cumulative incidence. It is a measure of the probability or risk of the disease or condition, i.e., what proportion of the population will develop an illness or condition during the specified time period. In contrast, the incidence rate indicates how quickly people become ill measured in people per year.

25. Rates Commonly Used in Epidemiology
Crude
Category specific
Age adjusted
Crude rates are those calculated for an entire population, such as the annual cancer mortality rate.
Category specific rates are based on the number of persons in the category and the number of cases occurring in that group, such as the age-specific cancer death rate.
Age adjustment allows for more appropriate comparisons when differences in age distribution in the two populations may mask real differences in the condition of interest.
Rates imply a change over time. For disease incidence rates, the change is from a healthy state to disease for a specified period of time.