Chapter 3
Chu Brest-Garo/Phanie/SuperStock
Epidemiologic Concepts Applied to
Community Health
Learning Objectives
After reading this chapter, you should be able to:
•• Define the term epidemiology.
•• Differentiate between descriptive and analytic epidemiology.
•• Trace the history of epidemiology.
•• Describe the applications of epidemiology to community health research.
•• Identify data sources used in community health.
•• Define at least three epidemiologic measures.
•• Analyze person, place, and time variables in descriptive epidemiological data.
•• Distinguish among types of epidemiologic study designs.
•• Describe the various levels of disease prevention as it applies to a community.
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Section 3.2 Definition and Scope of Epidemiology
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3.1 Introduction
This chapter will demonstrate how epidemiology—a fundamental public health discipline that involves the study of incidence, distribution, and control of diseases—contributes to the field of community health. We will demonstrate that epidemiology is important
because many circumstances that produce adverse health effects among community residents occur at the population
level; thus, it is vital to take a
population perspective when
examining individual health
outcomes in the community.
Epidemiology is unique because
of its focus on the health of populations, and, in this respect, it
differs from clinical medicine’s
involvement with individual
patients.
Furthermore, epidemiology provides a methodological foundation for the entire public health
field, which also takes the perSteve McAlister/Getty Images
spective of the population when
addressing the public’s health. Like detectives, epidemiologists use various methods for
Epidemiology embraces a spec- determining the often unknown causes of disease.
trum of tools for studying health
and illness. These methodologies include natural experiments, descriptive and analytic study designs (e.g., crosssectional, case-control, cohort, and experimental designs), and mapping technologies.
Epidemiologic research findings aid in the development of hypotheses for application to
the health of the community and the study of potential causal relationships.
Epidemiologic research is likened to detective work because the causes of many diseases—especially in their initial presentations—are unknown. There are many examples
of dangerous illness outbreaks in the community that began as mysteries. Some examples are Hantavirus in national parks, periodic episodes of foodborne illnesses, West Nile
virus, and resurgence of whooping cough (pertussis). Throughout the chapter, we will
discuss epidemiologic procedures and methodologies that aid in unraveling the causes of
mysterious disease outbreaks and health conditions that can afflict community members.
3.2 Definition and Scope of Epidemiology
This section covers basic epidemiologic concepts and describes how epidemiology aids
the community health field. Epidemiology is “[t]he study of the occurrence and distribution of health-related states or events in specified populations, including the study of the
determinants influencing such states, and the application of this knowledge to control the
health problems” (Porta, 2008, p. 81). Often these health-related states or events are called
outcome or dependent variables and include diseases, morbidity, injuries, disability, and
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Section 3.2 Definition and Scope of Epidemiology
CHAPTER 3
mortality. Recall from Chapter 1
that the term morbidity refers to
a state of disease or symptoms.
For example, while the mortality due to tuberculosis is on the
decline, morbidity associated
with this disease has increased.
Denver Post via Getty Images
A computer image of a GeneChip™, which holds a DNA
sequence on a glass slide. The GeneChip™ and other such
microarrays, which have been used in cancer research and
disease identification, are just one application of the Human
Genome Project’s success.
Epidemiology is a discipline that
describes, quantifies, and finds
possible causes for health phenomena in populations. These
postulated causal (etiologic)
mechanisms are known as determinants. Etiological factors are
those that contribute to a cause
of a disease or condition. For
example, smoking is a factor in
the production of the condition
of arteriosclerosis.
The results of epidemiologic studies aid public health practitioners in their quest to control
health problems like disease outbreaks. Epidemiology is an interdisciplinary enterprise
that draws upon diverse and related fields including clinical medicine, biostatistics, toxicology, the social sciences, and genetics. One example of this interdisciplinary approach
is the application of genetics and the Human Genome Project to epidemiology in order to
conduct research on the genetic bases for breast and ovarian cancer. The Human Genome
Project began in 1990 as an international effort to understand the sequence of chemicals in
DNA and to identify and map the thousands of genes in the human genome. Researchers
delivered a complete map in 2003, propelling further genomic research and exploration
into its medical applications.
While also concerned with the study and improvement of public health, community
health usually focuses on geographical areas rather than individuals with shared characteristics—although “community,” or more specifically, “biological community,” is broadly
defined. Epidemiology’s contributions to community health involve the discipline’s concern with populations, use of observational data, development of methodologies for study
designs, and application of descriptive and analytic study designs.
Concern With Populations
As noted, epidemiology is unique with respect to its focus on health outcomes in populations. Accordingly, some public experts call epidemiology “population medicine.” An
example of epidemiology’s focus on populations might be a study of the association of air
pollution with adverse health effects. A hypothetical epidemiologic study might examine
the occurrence of lung cancer mortality across counties or among regional subdivisions
known as census tracts—the smallest geographical unit for which population data might
be available in most countries. One of the driving hypotheses for the research might be
that lung cancer mortality is higher in areas with higher concentrations of “smokestack”
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Section 3.2 Definition and Scope of Epidemiology
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industries and associated emissions of air pollution in comparison with areas that have
lower levels of air pollution from these sources. The contrasting approach relative to population-based investigations would be a clinician’s diagnosis of an individual’s symptoms
(e.g., asthma and respiratory distress) associated with poor air quality.
Another example of a common concern with populations comes from total community
health studies such as the Framingham Heart Study in Massachusetts, which, as discussed
in Chapter 1, was able to identify common factors in cardiovascular disease by following
its development in a large population over time. This project, as well as several other
noteworthy epidemiologic investigations, studied risk factors for chronic diseases at the
community level. Examples of such research are provided later in the chapter.
Use of Observational Data
As a general rule, ethical issues, such as potential dangers to subjects due to participation
in a study, prevent researchers from conducting experiments with human beings. Obviously, it would be unethical for researchers to cause disease or other harm to human subjects. A classic example of failure to adhere to research ethics is the infamous Tuskegee
syphilis experiment. Between 1932 and 1972, the United States Public Health Service
studied the progression of
untreated syphilis in rural Black
men, neglecting to inform these
men that (1) they had syphilis
and (2) a cure—penicillin—had
been validated as early as the
1940s. The experiment participants instead thought the federal government was providing
free care. Researchers behaved
unethically in knowingly withholding both information and
appropriate treatment from ill
patients.
To overcome potential ethical challenges, epidemiologists
As scientists cannot ethically initiate disease in humans,
often study naturally occurepidemiological researchers observe the effects of naturally
ring situations in populations
occurring outbreaks such as the 1994 cholera outbreak pictured (e.g., disease outbreaks or other
here in Goma, Zaire.
adverse health outcomes in the
area). Alternatively, they might
observe the health effects of
community health policies applied to populations, such as mandatory lap belt use in cars,
smoking restrictions in workplaces, and fluoridation of water. Epidemiology is primarily an observational science that takes advantage of naturally occurring situations (or, in
some cases, policy changes applied to society) in order to study health outcomes. However, this observational approach does not rule out the use of experimental procedures in
Tom Stoddart Archive/Getty
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Section 3.2 Definition and Scope of Epidemiology
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intervention studies (e.g., randomized controlled trials and community trials). The simplest and most powerful epidemiological study is a randomized controlled trial (RCT), in
which researchers allocate people by chance alone to receive one of the clinical interventions. One arm of the study is the standard of care or the control group. The other participants are allocated to the intervention or case group. Researchers then compare the case
and control groups in terms of the outcomes measured as a result of the study.
Methodology for Study Designs
One of the goals of epidemiologic research is to portray the frequency and patterns of disease occurrence in the population and to link them with specific exposures. An exposure
(sometimes called an independent variable) is the potential causal factor in an epidemiologic
study. Exposures can include “environmental and lifestyle factors, socioeconomic and
working conditions, medical treatments, and genetic traits” (Porta, 2008, p. 89). In order
to examine the occurrence of disease outbreaks and environmentally caused diseases in
the population, the field of epidemiology uses several characteristic study designs. We
will discuss study designs in more detail later in the chapter.
Descriptive Versus Analytic Epidemiology
The two categories of epidemiology are descriptive epidemiology (represented by descriptive studies) and analytic epidemiology (represented by analytic studies). The former category is concerned with describing the occurrence of disease (or other health outcomes)
and the latter with identifying the causes of disease.
Descriptive epidemiology is defined as the characterization of disease occurrence in
populations according to classification by person, place, and time variables. Descriptive
epidemiologic studies are the first stage of an epidemiological study and include crosssectional studies (prevalence surveys). Regarded as a fundamental approach by epidemiologists, descriptive studies aim to delineate the patterns and manners in which disease
occurs in populations. Descriptive epidemiology examines the effects of factors like age,
education, socioeconomic status, availability of health care, race, and gender on outcomes,
or dependent variables. Other risk factors may also be included, such as behavioral factors like drug abuse, shift work, diet, and exercise patterns. An example of the results of a
descriptive study is the discovery of a correlation between people who work night shifts
and high blood pressure.
Analytic epidemiology is the process of using data gathered by descriptive experts to
study patterns suggesting causes of diseases and other health conditions. Some typical
examples of questions analytic epidemiologists ask are: What caused a foodborne disease
outbreak? Do toxic chemicals cause cancer? What is the effect of diet on heart disease and
diabetes? For the most part, analytic epidemiologic studies employ observational studies
(e.g., case-control studies and cohort studies). They also make use of intervention studies. In a case-control study, subjects are enrolled on the basis of whether they have had
the disease to determine whether there is an association existing between the disease and
exposure. Cohort study subjects are enrolled based on their membership in a controlled
subpopulation or category of the population (e.g, racial groupings).
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CHAPTER 3
Section 3.3 Brief History of Epidemiology
3.3 Brief History of Epidemiology
The history of epidemiology extends over many centuries, beginning with the contributions of the ancient Greeks during the classical period. Some of the landmarks in the history of epidemiology overlap the history of public health. Chapter 1 described historically
noteworthy and frightening epidemics that threatened the very existence of humanity.
These epidemics included the
Black Death, which occurred
between 1346 and 1352, and
the great influenza pandemic
that coincided with World War I
early in the 20th century. Other
significant historical developments (also discussed in Chapter 1) include Edward Jenner’s
development of an effective
vaccine against smallpox and
the work of Robert Koch that led
to the identification of microbial
agents in human disease. In
1928, Alexander Fleming discovered that certain molds had
antibiotic properties. As a result
of this research, the antibiotic
Science and Society/SuperStock
penicillin became available at
the end of World War II. Table A major milestone in epidemiology was Alexander Fleming’s
3.1 lists several other impor- contribution of penicillin in 1928. This photograph shows Fleming
tant milestones in the history of (right) at St. Mary’s Hospital in Paddington, London, 1945.
epidemiology.
Table 3.1: Key events in the history of epidemiology
400 BCE
Hippocrates publishes On Airs, Waters,
and Places
Implies that the physical environment is
associated with human illness
1662
John Graunt publishes Natural and Political
Observations
Pioneers work in vital statistics (data that
pertain to births and deaths)
Mid-1800s
John Snow investigates a cholera outbreak
in London
Uses the method of natural experiment to
examine the causes of cholera
1964
The Surgeon General’s report Smoking and
Health is published
States that smoking is the cause of lung
cancer
1974
The Lalonde Report, A New Perspective on
the Health of Canadians, is published
Initiates large community health studies;
community interventions are implemented
Some events are further described below.
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Section 3.3 Brief History of Epidemiology
CHAPTER 3
•
Hippocrates Links Environment and Illness. In 400 BCE, the Greek physician
Hippocrates suggested in his treatise On Airs, Waters, and Places that disease
could be linked with a person’s physical environment—marking perhaps the
earliest instance that logic, rather than mysticism, was used to explain an illness’s origin (Friis & Sellers, 2009). Hippocrates wrote that the seasons, the
influence of the sun, the quality of water, and the elevation at which people live
were factors in health.
• John Graunt Describes Trends in Vital Statistics. In 1662, John Graunt published Natural and Political Observations Mentioned in a Following Index; and Made
Upon the Bills of Mortality. Graunt described various details of birth and death
data, including seasonal variations and infant mortality (Glass, 1963). Public
health historians regard Graunt’s work as among the first to organize mortality
data in tables in order to discern trends in births and deaths from specific causes
(Friis & Sellers, 2009).
• John Snow Maps Cholera Outbreak. As described in Chapter 1, during the
mid-19th century, John Snow investigated a cholera epidemic that occurred in
Broad Street, Golden Square, London, eventually linking the epidemic to contaminated water supplies (Snow, 1855). His classic observational study reflects
many of the features of modern epidemiologic inquiry: mapping the location of
instances of disease and tabulating fatalities. The natural experiment described
in Snow on Cholera (Frost, 1936/1965), which includes two papers by John Snow
and a biographic memoir, reported the effects of changing to a cleaner water
source on reduction in the occurrence of cholera in the Golden Square district
of Soho. Despite the absence of knowledge about the nature of microbial agents
during his time, Snow made numerous insightful discoveries that could be
applied subsequently to the control of epidemics.
• Smoking and Lung Cancer Published. The 1964 Surgeon General’s Report
Smoking and Health stated that cigarette smoking is a cause of lung cancer in
men (United States Department of Health and Human Services). This report
caused a global reaction when it first appeared. The report listed five “criteria
of judgment” that were used to judge the statistical causal significance of the
association between smoking and lung cancer. These criteria were strength of
association, time sequence, consistent relationship upon repeated exposure to
smoking, specificity of association, and coherence of explanation. The authors of
the report said that accumulated research conducted up to the time of the report
tended to support these five criteria of judgment in testing causal associations
between this health behavior and morbidity due to lung cancer.
Contemporary Epidemiology
In addition to the ongoing Framingham Heart Study described earlier in the book, during
the second half of the 20th century, several renowned large-scale community trials have
addressed modifying risk factors for coronary heart disease in order to reduce the impact
of this leading cause of mortality. Other trials have been directed toward smoking cessation, prevention of HIV infections, and prevention of chronic disease. One of the most
noteworthy community trials was the Stanford Five-City Project (Farquar et al., 1985), a
major community trial designed to lower risk of cardiovascular diseases. Both the treatment cities (Monterey and Salinas) and control cities (Modesto and San Luis Obispo) were
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Section 3.3 Brief History of Epidemiology
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located in California. The intervention took place over 6 years and used a multifactor risk
reduction education program that used multiple communication channels (e.g., releases
in the media) and intervention settings.
Epidemic Disease and Community Health
Many current and historical examples of epidemics exist, including recent epidemics of
influenza, West Nile virus (covered later in the chapter), whooping cough, and foodborne
illnesses. In order to monitor the presence of epidemic disease, local and state public
health departments and the CDC have implemented surveillance systems. These involve
the periodic reporting of conditions, known as reportable and notifiable diseases, and compiling statistics on their occurrence. When the incidence of a reportable disease such as
influenza exceeds statistical limits, this event suggests that an epidemic is under way.
The occurrence of a case of a new disease or of a communicable disease that has been absent
from a population for some time (because it was previously brought under control) must be
investigated by health officials. The appearance of two cases of such a disease may be considered an epidemic. An example would be doctors discovering new cases of polio, which
has been eradicated in the United States. The initial appearance in the early 1980s of a few
cases of pneumonia (caused by Pneumocystis carinii) among gay men in Los Angeles signaled
the beginning of the AIDS pandemic (Porta, 2008). More recent
examples of pandemics are the
2007 avian influenza and the
2009 H1N1 influenza pandemic.
Karen Kasmauski/Science Faction/SuperStock
An epidemic is endemic when the agents of disease are specific
to a certain region. In this photo, a deer mouse—thought to be
the vector spreading a hantavirus in California—is captured for
further study.
Another term used in descriptions of epidemics is endemic,
which means that a condition is
habitually present in a particular geographic area. An example
is whooping cough, an endemic
disease in the United States.
Often, diseases are endemic
because of local environmental
conditions such as disease vectors that infest a geographic area.
One such disease is endemic
plague, which is associated with
certain types of ground squirrels and some other rodents that
inhabit California.
Numerous examples of epidemics and pandemics have occurred during the 20th century
and, more recently, during the first decades of the current century.
•
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The 1918 Influenza Pandemic. This pandemic, which also goes by the name
“Spanish flu,” occurred from 1918 to 1919 and killed an estimated 50 to 100 million people worldwide. It is estimated that one-third of the world’s population
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CHAPTER 3
Section 3.3 Brief History of Epidemiology
•
became infected and developed symptoms. In contrast with most influenza outbreaks, the victims of the Spanish flu were mainly young healthy adults (aged
20 to 40) who accounted for nearly half of the mortality toll in this pandemic
(Friis & Sellers, 2009).
Whooping Cough (Pertussis). Since the beginning of the 21st century, whooping cough (pertussis) has been on the rise in the United States, reaching epidemic proportions by 2012. Some experts in infectious disease attribute this
growing epidemic to a weakened immune status of immunized people. In other
words, immunity waned after a period of time because available vaccines did
not induce long-term immunity. Many regions in the United States are enduring the pertussis epidemic, but particularly noteworthy is Washington State.
The mid-2011 Washington State pertussis epidemic produced an alarming rise
in case numbers—to the highest level reported since 1942 (MMWR, 2012). This
prompted the state Secretary of Health to declare an epidemic on April 3, 2012.
As of June 16, 2012, the reported caseload had increased 1,300% from 180 to
2,520 cases from the year before; laboratory tests, or epidemiological evidence,
confirmed about four-fifths of the 2,520 cases. Figure 3.1 demonstrates the number of confirmed and probable pertussis cases reported by Washington State to
the CDC. The figure indicates the number of cases by the week of onset from
January 1, 2011, to June 16, 2012 (CDC, 2012a).
Figure 3.1: Confirmed and probable pertussis cases in Washington State
(January 1, 2011–June 16, 2012)
2011
250
2012
Number of Cases
200
150
100
50
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Onset Week
Source: Adapted from Centers for Disease Control and Prevention. (2012, July 20). Pertussis Epidemic—Washington, 2012. Morbidity
and Mortality Weekly Report, 61(28), 517–522. Retrieved October 27, 2012 from http://www.cdc.gov/mmwr/preview/mmwrhtml
/mm6128a1.htm
Weakened immune status of immunized people is a possible cause for the rise of pertussis cases in the
United States.
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Section 3.4 Applications of Epidemiology for Community Health
•
CHAPTER 3
Multistate Fungal Meningitis Outbreak. During the fall of 2012, the CDC and
the Food and Drug Administration (FDA) investigated cases of fungal meningitis associated with a spinal injection of a steroid medication used to treat joint
and back pain. Testing of the medication revealed the presence of noncontagious
fungal contamination. The first case was reported in a patient who had received
a spinal injection in a Tennessee ambulatory surgical center. Subsequently, the
number of cases grew to 137, with 10 deaths. Eventually, investigators found
that meningitis was associated with three production batches (lots) of a steroid
medication supplied by The New England Compounding Center. The result
was that the three lots were voluntarily recalled from the marketplace.
3.4 Applications of Epidemiology for Community Health
The late British epidemiologist Professor Jerry Morris wrote Uses of Epidemiology. Morris
published this classic book in 1957, a time in Britain’s history of growing recognition of
social and economic factors in health. Morris’s prescient account of seven uses of epidemiology remains apropos to modern community health issues. Refer to Table 3.2 for a list
of the seven uses.
Table 3.2: The seven uses of epidemiology*
1. Study the history of the health of populations
2. Diagnose the health of the community
3. Study the working of health services
4. Estimate individual risks
5. Identify syndromes
6. Complete the clinical picture
7. Search for causes
* Shaded items are discussed in the text.
Data from Morris, J. N. (1957). Uses of epidemiology.
The following sections highlight four uses of epidemiology that are especially relevant to
the community health field. The uses include studying historical trends in health, describing the health of the community, assessing risks to the individual, and examining the
causes of disease in the community.
Study Historical Trends
The historical use of epidemiology refers to the study of time trends in health and illness. One example of the historical use of epidemiology is the study of changes in disease frequency (secular trends) over long time periods (Smith, 2001). In general, chronic
conditions have replaced acute infectious diseases as the major causes of morbidity and
mortality in contemporary industrialized societies. In Chapters 1 and 2, this change was
called the epidemiologic transition. With respect to the health of the community, it is apparent that chronic health conditions (e.g., obesity, nutritional deficiency, and heart disease)
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Section 3.4 Applications of Epidemiology for Community Health
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have now become major challenges to the population’s health, although infectious diseases remain important causes of morbidity and mortality.
Describe the Health of the Community
Describing the health of a community pertains to the identification of demographic,
behavioral risks, and environmental factors in a community that impact health-related
outcomes. Using epidemiology to describe the health of a community relates to Healthy
People 2020’s overarching goals:
1. Attain high-quality, longer lives free of preventable disease, disability, injury,
and premature death.
2. Achieve health equity, eliminate disparities, and improve the health of all groups.
3. Create social and physical environments that promote good health for all.
4. Promote quality of life, healthy development, and healthy behaviors across all
life stages. (HealthyPeople.gov, 2013)
Examples of categories of variables that affect community health include demographic
and social composition, community infrastructure, and environmental characteristics.
Refer to Spotlight: Variables that Affect the Health of the Community for more detailed information regarding these variables and how they could affect the health of a community.
Spotlight: Variables that Affect the Health of the Community
•• Demographic and social variables: Those in poorer communities often suffer greater ill health
and shorter life expectancies.
1. Age and sex distribution
2. Socioeconomic status
3. Family structure, including marital status and number of single-parent families
4. Racial, ethnic, and religious composition
•• Variables related to community infrastructure: Poorer facilities and physical environment directly
and indirectly affect health behaviors and subsequently the risk of developing chronic disease.
1. Availability of social and health services including hospitals, emergency rooms, and community clinics
2. Quality of housing stock including presence of lead-based paint and asbestos
3. Social stability (residential mobility), such as community policing and employment opportunities
•• Health-related outcome variables: Outcome variables measure dimensions of health status in
the community.
1. Homicide and suicide rates
2. Infant mortality rate
3. Mortality from selected conditions
4. Scope of chronic infectious diseases
5. Alcoholism and substance abuse rates
6. Teenage pregnancy rates
7. Occurrence of sexually transmitted diseases
8. Birthrate
(continued)
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Section 3.4 Applications of Epidemiology for Community Health
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•• Environmental variables: The physical environment can provide protective factors that maintain
or improve the health of a population.
1. Air pollution from stationary and mobile sources
2. Access to parks and recreational facilities
3. Availability of clean water
4. Availability of markets that supply healthful groceries
5. Number of liquor stores and fast-food outlets
6. Nutritional quality of foods and beverages vended to schoolchildren
7. Soil levels of radon
Source: Adapted from Friis & Sellers. (2009). Epidemiology for public health practice (4th ed.). Sudbury, MA: Jones and Bartlett
Publishers.
Estimate Individual Risks
Epidemiologic methods are linked closely with the field of risk assessment, which seeks to
identify potential hazards and analyze potential fallout should a hazard occur. In environmental risk assessment, epidemiological studies can be used to estimate potential health
risk factors in the environment and how they are related, or associated. An example of this
use of epidemiology is that of projections about the probability of developing lung cancer
among smokers versus nonsmokers.
Determine the Etiology of Disease
Among the most important uses
of epidemiology is uncovering
the causes (etiology) of disease.
When describing the causes of
disease, epidemiologists employ
exposures and risk factors. Epidemiologic research explores
possible associations among
exposures and health outcomes through the application
of appropriate study designs,
measures, and other methodologies. The use of appropriate
study designs provides insight
Kallista Images/SuperStock
into whether an observed association is due to chance or is a The pink in this CT scan represents cancerous growth in
causal association. A classic the lungs of a 54-year-old man. In the quest to discover the
example of using epidemiology etiology of lung cancer, epidemiological researchers were able
to study the etiology of disease to identify smoking as a major risk factor.
is research on smoking and lung
cancer. Smoking is a risk factor
for lung cancer, and understanding the disease etiology will help a clinician prescribe the
most effective preventive or therapeutic interventions.
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Section 3.5 Data Sources Used in Community Health
The findings of epidemiologic research aid in policy development by providing methodological skill sets and contributing to the fund of information needed to guide informed
decision making. Backed by supportive, evidence-based epidemiologic research, policy
actors are able to introduce health-related policies that protect the community’s health.
Legislators and government officials are charged with the responsibility of creating policies and enacting and enforcing many laws that have substantial impacts on public health.
Following the adoption of desired health policies and programs, public health professionals can apply epidemiologic methods to the evaluation of program effectiveness.
An example of health policies that have been implemented in a community comes from
the California city of Long Beach, which enacted a no-smoking ordinance, LBMC 8.68
(City of Long Beach, 2013). Within the past decade, the city of Long Beach has created
smoke-free indoor public areas, including bars and restaurants, pool halls, office buildings, and elevators and laundry rooms. No-smoking areas include parks; beaches; bus
stops; farmer’s markets; and areas within 20 feet of state, county, and city buildings. The
rationale for this ordinance stemmed from evidence that linked adverse health effects
with secondhand cigarette exposure.
In 2011, the state of California passed Senate Bill (SB) 332. This bill “prohibits any person from smoking a cigarette, cigar, or other tobacco-related product, or from disposing
of cigarette butts, cigar butts, or any other tobacco-related waste, within a playground”
California Legislation Senate Bill 332, 2011, para. 1). Furthermore, SB 332 authorizes “a
landlord of a residential dwelling unit to prohibit the smoking of tobacco products on
the property, in a dwelling unit, in another interior or exterior area, or on the premises
on which the dwelling unit is located” (California Legislation Senate Bill 332, 2011,
para. 3).
3.5 Data Sources Used in Community Health
This section provides examples of data sources for the description and analysis of the state
of a community’s health, as well as measures of morbidity and mortality that are derived
from these data sources. Given the quantitative nature of the discipline, the practitioners
of epidemiology pay close attention to the quality of data and measures used to assess
disease occurrence. Table 3.3 provides examples of data sources for use in epidemiologic
and community health research.
Table 3.3: Examples of epidemiologic data sources
Vital statistics data (e.g., data on births and deaths) Morbidity surveys of the population
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Notifiable and reportable disease statistics
National Health Interview Survey
Case registries (e.g., cancer registries)
Behavioral Risk Factor Surveillance Survey
United States Census (demographic data)
The World Health Organization (WHO)
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CHAPTER 3
Section 3.5 Data Sources Used in Community Health
Vital Statistics Data
Types of information included in this category include data collected on vital events
(births and deaths) by the vital registration system of the United States. Statistics derived
from the vital registration system are computed from the data that are collected routinely
on all births and deaths that occur in the United States. Birth certificate data are needed to
calculate birth rates. In addition, this information may also contain data about a range of
conditions that could affect newborn children, including conditions present during pregnancy, congenital malformations, obstetric procedures, birth weight, length of gestation,
and demographic background of mothers. Death certificate data in United States include
demographic information about the deceased person and information about the cause of
death, including the immediate cause and contributing factors (Friis & Sellers, 2009).
Notifiable Diseases and Surveillance Programs
By legal statute, physicians and other health care providers must report cases of diseases
known as reportable and notifiable diseases. The reporting individuals send the information to local agencies (e.g., health departments), from which it flows to state and federal
levels. Notifiable disease reporting at the community level protects the public’s health by
ensuring the proper identification and follow-up of cases of disease. These diseases are
usually infectious and communicable ones that might endanger a population. See Table
3.4 for examples of these kinds of diseases.
Table 3.4: Examples of reportable and notifiable diseases in the United States
Anthrax
Human Immunodeficiency Virus (HIV)
Arboviral diseases
Meningococcal disease
Botulism
Rabies
Cholera
Salmonella
Hepatitis
Tuberculosis
Reportable and notifiable disease reports contain official statistics in tabular and graphical
form. Statistics are collected and compiled from reports sent by state health departments
and U.S. territories to the National Notifiable Diseases Surveillance System, which is operated by the CDC in collaboration with the Council of State and Territorial Epidemiologists.
Case Registries
A registry is a centralized database for collection of information about a particular disease.
Registries are used commonly for the compilation of statistical data on cancer, although
other types of disease registries exist. An example of a population-based cancer registry
is the Surveillance Epidemiology and End Results (SEER) program, which has provided
unique and valuable data on cancer survival, incidents, and treatment (Friis & Sellers,
2009).
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CHAPTER 3
The United States Census Bureau
The Census Bureau is an invaluable resource for characteristics of the United States population. Census data provide information about the denominators used in the calculation
of measures of morbidity and mortality. (A denominator is the number of parts to which
the whole is divided.)
The decennial (10-year) census counts every resident in the country. One constitutionally mandated use of data from the decennial census is to determine the number of seats
each state has in the House of Representatives. Census 2010, the most recent census to
date, provides detailed information about the country’s entire population, including the
variables of age, sex, race, and ethnicity, as well as housing characteristics and household
information (United States Census, 2010). Researchers can extract this information from
census data sets in order describe the sociodemographic and other characteristics of a
specific community.
Morbidity Surveys of the Population
Morbidity surveys are procedures for collecting information on the health status of a population group by using self-administered questionnaires, interviews, and direct examinations of participants (Friis & Sellers, 2009). Some of the purposes of morbidity surveys
are to determine the frequency of chronic and acute diseases and disability, to obtain
measurements of bodily characteristics, to conduct physical examinations and laboratory
tests, and to probe other health-related characteristics of special concern to those who
sponsor the survey.
The National Health Interview Survey
An example of a morbidity survey is the National Health Interview Survey (NHIS). Conducted by the National Center for Health Statistics, the NHIS is a household health interview survey that has been collecting data on a broad range of health topics since 1957
(Centers for Disease Control, 2012a).
Behavioral Risk Factor Surveillance Survey
The Behavioral Risk Factor Surveillance System (BRFSS) is the world’s largest, ongoing
telephone health survey system. BRFSS began collecting information on risk behaviors
and tracking health conditions in the United States in 1984. Results from the BRFSS have
provided data to track a community’s health status, access to health care, and progress
toward achieving state and national health objectives.
Data From the Centers for Disease Control and Prevention
On July 1, 1946, the Communicable Disease Center (now known as the Centers for Disease
Control and Prevention) was established to save lives and protect the population of the
United States. Since its inception, the CDC has been instrumental in responding to natural
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Section 3.6 Measures of Disease Occurrence
disasters, identifying the H1N1 influenza virus, investigating massive foodborne outbreaks,
providing support for laws related to public health, and recommending guidelines for public health purposes (CDC, 2012b). As part of its ongoing functions, the CDC provides a
wealth of epidemiologic data relevant to community health. Some of these data are available online from the CDC website (www.CDC.gov) and from CDC-sponsored publications:
•
•
•
Morbidity and Mortality Weekly Report (MMWR)
National Center for Health Statistics Data Briefs
National Vital Statistics Reports (NVSS)
World Health Organization
The World Health Organization (WHO) was established on April 7, 1948, within the
United Nations system. The WHO aims to provide leadership on global health matters,
shape the health research agenda, set norms and standards, articulate evidence-based policy options, provide technical support to countries, and monitor and assess health trends
(World Health Organization, 2012). A wealth of international health-related data from
WHO is available by accessing the organization’s website at www.who.int.
3.6 Measures of Disease Occurrence
Community health researchers use measures of disease occurrence for a variety of purposes. These include identifying epidemics and other health problems, assessing the
effectiveness of prevention programs, finding health disparities, and showing associations between exposures and health outcomes. Fundamental measures discussed in this
section are counts, ratios, and proportions. Two examples of more complex epidemiologic
measures are prevalence and incidence—two terms that often are used incorrectly in the
professional literature. Other more complex measures are rates such as incidence rates
and mortality rates. Refer to Table 3.5.
Table 3.5: E xamples of common epidemiologic measures encountered in the
medical literature
Count
Case fatality rate
Ratio
Incidence
Proportion
Rate
Prevalence
Incidence rate
Point prevalence
Mortality rate
Fundamental Measures
The simplest and most commonly used quantitative measure in epidemiology is a count.
A count refers to the total number of cases of the disease or other health phenomenon
being studied. One example could be the number of falls in a nursing home during a
1-month period.
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A ratio shows the relationship between two comparable amounts. For example, a sex ratio
would compare the number of male cases to female cases. A ratio can also be expressed as
a value, when one divides the first quantity by the second. Using the earlier example, the
sex ratio of births in the United States is greater than one, indicating that more boys than
girls are born.
A proportion is a type of ratio and may be expressed as a percentage, indicating the part
of a whole. For example, only a small proportion of graduates fail to find employment.
Prevalence and Incidence
The term prevalence is a measure of disease occurrence meaning the total number of cases
of disease. Point prevalence is defined as the frequency of disease or other condition at a
given point in time. For example, the prevalence of influenza or pertussis is the number of
cases of that disease in a population at some designated time, such as on a particular date.
Point prevalence is a proportion.
The term incidence refers to a count of new cases of disease among a group within a time
period. An example of incidence is the number of new cases of influenza that occur within
a time interval such as a 2-week period.
Rates
A rate is a measure that includes time as a part of the denominator. As an example, an
incidence rate describes a disease’s rate of development within a group during a specified
period. Another commonly used rate is a mortality rate.
A rate consists of a numerator (the frequency of new cases of disease during a time period)
and a denominator, which is a unit size of population. The population could be of a community, a state, or the entire country. To calculate a rate, one must consider two points in
time: the beginning of the period and the end of the period during which the new cases
occur. Rates are expressed as numbers per unit size of population (e.g., 10 per 1,000 persons in a population or some other number per unit size of population). Incidence can be
expressed as a rate, but prevalence is never a rate.
An incidence rate uses only the frequency of new cases that occur during a time period in
the numerator. Consequently, individuals who already have the disease are not included
in the numerator. The denominator for incidence rates is the population at risk: those who
are capable of developing the disease either because they are not immune or for some other
reason. An incidence rate includes (1) a numerator—the number of new cases; (2) a denominator—the population at risk; and (3) time—the period during which the cases amass.
The mortality rate refers to the number of deaths during a given year divided by the size
of a reference population (or denominator) during the middle of the year in question.
Another measure of mortality is the case fatality rate, which is expressed as a percentage.
The case fatality rate refers to the number of deaths caused by disease among those who
have the disease during a time period. To better understand this concept, consider this
example: If 50 people became infected with Hantavirus and 8 of them died, the case fatality rate would be (8 ÷ 50) × 100 = 16%.
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Section 3.7 Descriptive Epidemiology
3.7 Descriptive Epidemiology
Descriptive epidemiology is one of the most useful applications of epidemiology. The
three broad objectives of descriptive epidemiology are to (1) permit evaluation and comparisons of trends in health and disease among populations or subpopulations (these
can include countries and subgroups within countries); (2) provide a basis for planning,
provision, and evaluation of health services in order to facilitate the efficient allocation
of resources; and (3) identify problems to be studied by analytic methods and to suggest areas that may be fruitful for investigation. The work of health care researchers with
respect to the acquired immune deficiency syndrome (AIDS) epidemic in Long Beach,
California, illustrates how these objectives could be applied to a community. Refer to Case
Study: The AIDS Epidemic in Long Beach, California for more on this issue.
Case Study: The AIDS Epidemic in Long Beach, California
Photononstop/SuperStock
The city of Long Beach might have implemented
an education session such as this as one on its
HIV/AIDS prevention strategies.
The first objective in descriptive epidemiology
is to evaluate trends. In 2008, the city of Long
Beach, California, released a report describing the HIV/AIDS epidemic between 1981 and
2008. The report identified a cumulative incidence rate of 1259.74 AIDS cases per 100,000
residents during that period—more than double the rate for the entire state (449.69 cases
per 100,000) (City of Long Beach Department
of Health and Human Services, 2008). By identifying this trend and comparing it to other
populations, the city was able to demonstrate
that AIDS remains a significant public health
issue in the Long Beach community.
The city proceeded to the second objective by
assessing community HIV/AIDS prevention and
care needs in order to enhance its local HIV/
AIDS services. Information gathered from the assessment, which used client and provider surveys and
client focus groups, aided in planning and implementing HIV/AIDS education and prevention services.
As for the third objective, findings from this assessment assisted program planners with identifying
areas for further consideration such as which populations are target populations and resource allocation
needs (Friis & Sellers, 2009).
1 City of Long Beach HIV/AIDS Monitoring Report, December 2008. Available at: http://www.longbeach.gov/civica/filebank/
blobdload.asp?BlobID=22225. Accessed October 18, 2012.
2 City of Long Beach, HIV/AIDS Prevention and Care Needs Assessment, 2006. Available at: http://www.longbeach.gov/civica/
filebank/blobdload.asp?BlobID=3096. Accessed October 18, 2012.
Descriptive epidemiology refers to the classification of diseases and other health characteristics according to person, place, and time. Examples of person variables are demographic
characteristics such as sex, age, race, and ethnicity. Place variables denote geographic
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Section 3.7 Descriptive Epidemiology
CHAPTER 3
locations including countries or a specific country, regions within countries, and places
where localized patterns of disease may occur. Time variables include occurrence of health
outcomes by decades, years, months, weeks, or days. Examples of descriptive epidemiologic data for the variables of person, place, and time are given in the following sections.
Person Variables
This discussion will focus in depth on age, sex, race and ethnicity, and socioeconomic status, which are among the most important descriptive epidemiologic variables. However,
we will briefly discuss marital status, nativity and migration, and religious background,
as these also can influence health.
Marital status—whether one is single or nonmarried, married, or unmarried but living
with a partner—has been found to be a protective factor in health. Rates of morbidity and
mortality vary according to marital status, and evidence suggests that married adults tend
to be healthier overall than nonmarried adults.
Migration is related to patterns of chronic disease and life expectancy. When persons
immigrate to a new country, often they acquire the health-related characteristics of the
inhabitants of the host country, perhaps as a result of changes in diet and lifestyle. The
process of adopting the cultural practices of the host country is known as acculturation. For
example, Japanese immigrants (a low-risk group for coronary heart disease in their home
country) who relocated to the United States and became acculturated had a higher incidence of coronary heart disease than immigrants who were less acculturated. The healthrelated aspects of immigration are significant for the many American communities that
have large immigrant populations.
Religious background affects personal lifestyle characteristics, and consequently is associated with variations in morbidity and mortality. For example, the adherents to some
religious groups (e.g., Seventh-Day Adventists who practice vegetarianism) have lower
rates of cancer and chronic disease mortality in comparison with the general population.
Age
Age is one of the most important descriptive epidemiologic variables for showing the distribution of health outcomes. Some ways to classify age are according to 10-year intervals;
larger age categories are used as well (e.g., children and teenagers, young adults, older
adults, and the elderly). Age is related to many health outcomes, including morbidity
and mortality from infectious diseases, chronic diseases, unintentional injuries, and disabilities. For example, the chronic diseases (cancer, heart disease, and diabetes) affect the
elderly more frequently than the young. Another example of age effects is the tendency
for cancer mortality to increase linearly with age. In comparison, the leading cause of
mortality among younger individuals is unintentional injuries. Still other examples of age
effects are variations in both suicide and homicide mortality among different age groups.
According to 2009 data from the CDC, those between 18 and 24 years old had the highest
rate of homicide, while those between 45 and 54 years old had the highest rate of suicide.
The suicide rate was higher than the homicide rate among those 25 years old and older;
this difference increased with age: While the rate of suicide for those between 25 and 44
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Section 3.7 Descriptive Epidemiology
was nearly twice the rate of homicide, the suicide rate was nearly seven times the homicide rate for those 65 and older (Centers for Disease Control, 2012c; see Figure 3.2).
Figure 3.2: Suicide and homicide mortality among U.S. age groups
Suicide
Rate per 100,000 Population
25
Homicide
20
15
10
5
0
0–9
10–17
18–24
25–44
45–54
55–64
³65
Age
Source: Adapted from Centers for Disease Control and Prevention. (2012c, July 20). QuickStats: Suicide and homicide rates, by age
group—United States, 2009. Morbidity and Mortality Weekly Report, 61(28), 543. Retrieved October 26, 2012 from http://www.cdc.gov/
mmwr/preview/mmwrhtml/mm6128a8.htm?s_cid=mm6128a8_w
Suicide and homicide rates are examples of age effects on health outcomes.
Sex
Many outcomes in morbidity and mortality reflect sex or gender differences. One example
of sex differences is in overall mortality, which is higher for males than females for all
causes. Men also have higher mortality rates than women for cancer of the lung and bronchus. Although men have higher rates of mortality, the reverse is true for morbidity rates,
which for many acute and chronic conditions are higher for women than for men.
An example of sex differences in health outcomes is the occurrence of heat-related deaths
by sex in the United States during the period 1999–2010 (MMWR, 2012). See Figure 3.3.
The CDC noted that
[f]rom 1999 to 2010, a total of 7,415 deaths in the United States, an average of 618 per year, were associated with exposure to excessive natural
heat. The highest yearly total of heat-related deaths (1,050) was in 1999 and
the lowest (295) in 2004. Approximately 68% of heat-related deaths were
among males. (Centers for Disease Control, 2012d)
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Section 3.7 Descriptive Epidemiology
Number of Deaths
Figure 3.3: Heat-related deaths by sex
800
Male
700
Female
600
500
400
300
200
100
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
0
Year
Source: Adapted from Centers for Disease Control and Prevention. (2012d, September 14). QuickStats: Number of heat-related deaths,
by sex—National Vital Statistics System, United States, 1999–2010. Morbidity and Mortality Weekly Report, 61(36), 729. Retrieved from
http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6136a6.htm?s_cid=mm6136a6_w
One of the many ways in which morbidity and mortality outcomes differ is by sex. Different health and
environmental circumstances affect males and females differently. For example, men tend to suffer
from heat-related deaths at a higher rate than women, as is shown in this graph.
Race
One of the well-developed systems for classifying race has been implemented by the
United States Census Bureau (2011). This system uses five categories plus a “some other
race” category. Respondents to the 2010 Census questionnaire could self-identify their
racial group membership and also select more than one racial group. The racial categories
used in the 2010 questionnaire were White, Black or African American, American Indian
or Alaska native, Asian, and native Hawaiian or other Pacific Islander. Respondents were
also requested to indicate whether they were of Hispanic origin; persons of Hispanic origin could be of any race. For example, Hispanics can also be classified as Black, White, or
Asian depending on the race with which they identify themselves.
The United States is becoming increasingly diverse with respect to race and ethnicity.
Racial characteristics are associated with substantial variations in several indices of morbidity and mortality, such as birth rates. One application of data on racial differences in
health outcomes is to identify health disparities and develop community programs for
groups at greatest risk of adverse health outcomes. We will now discuss a study of racial
disparities in teenage birth rates.
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Figure 3.4 compares teenage birth rates for racial and ethnic groups in the United States.
Although all of these groups have reflected declining trends in teenage birth rates, significant disparities in teenage birth rates exist in the country. The CDC reported that the
birth rate in 2011 for females aged 15 to 19 years was the lowest rate ever recorded in the
United States. The rate declined by 25% between 2007 and 2011 from 41.5 to 31.3 births
per 1,000 women in this age group. Also showing declines were non-Hispanic White, nonHispanic Black, American Indian or Alaska Native, and Asian or Pacific Islander teenagers. Hispanic teenage girls had the greatest decline in teenage birth rates. Nevertheless,
researchers pointed out that the teenage birth rate was higher than that found in many
other affluent countries (Centers for Disease Control and Prevention, 2012e).
Figure 3.4: Birth rates for females aged 15–19 years by race and ethnicity
100
2007
90
2011
Births per 1,000
80
70
60
50
40
30
20
10
0
All
Races
NonHispanic
White
NonHispanic
Black
Hispanic
American
Asian/
Indian or
Pacific
Alaska Native Islander
Race/Ethnicity
Source: Centers for Disease Control and Prevention. (2012e, October 26). QuickStats: Birth rates for females aged 15-19 years, by race/
ethnicity–National Vital Statistics System, United States, 2007 and 2011. Morbidity and Mortality Weekly Report, 61(42), 865. Retrieved
from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6142a8.htm
Tracking teenage birth rates is one way to identify health disparities based on racial and ethnic differences. The graph indicates the rate of teenage births by race and ethnicity, and then compares these
rates across the six race and ethnicity categories established by the United States Census Bureau.
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Section 3.7 Descriptive Epidemiology
CHAPTER 3
Socioeconomic Status
The variable of socioeconomic status is defined by one’s type of occupation, income level,
and amount of formal education. By definition, individuals who have higher income levels, higher education levels, and are employed in occupations such as the learned professions have higher socioeconomic statuses than those who are lower with respect to these
three characteristics. Low socioeconomic status is associated with higher rates of morbidity from infectious and chronic conditions, and generally a greater frequency of adverse
health outcomes in comparison with high socioeconomic status. We discussed the association between low socioeconomic status and health disparities in Chapters 1 and 2.
Place Variables
The variable of place includes international, within-country, regional, and local patterns
in health outcomes. Within-country variations can be subdivided further into urban and
rural comparisons. The United States Census Bureau defines urbanization by using geographic locations called Metropolitan Statistical Areas (MSAs), which are urbanized areas
based on the number of inhabitants in a particular area. An MSA is a geographical region
with a relatively high population density at its core and close economic ties throughout
the area. It is typically centered around a single city that wields substantial influence on
the region (e.g., Chicago).
International
Numerous examples of international variations in health outcomes can be cited, especially with respect to infectious diseases (e.g., cholera, malaria, and vaccine-preventable
conditions). An example of international variations is the occurrence of measles, which
has been eradicated in the United States. In 2010, the World Health Assembly identified
goals (e.g., increasing the first dose coverage with measles vaccine to 90% or more within
a country) to be accomplished by 2015 as benchmarks toward the eradication of measles
worldwide. The CDC noted that Ethiopia, Kenya, and Somalia are three countries in the
Horn of Africa where measles remains endemic (CDC, 2012f). Outbreaks occurred during
2010 through 2011, primarily among unvaccinated persons. The respective numbers of
cases were 9,756, 2,566, and 16,135. The cases in Ethiopia and Kenya had wide age distributions, whereas the cases in Somalia occurred mainly among children younger than
5 years of age. Figure 3.5 presents annual reported measles incidence, by administrative
area, in the Horn of Africa during 2010–2011.
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Section 3.7 Descriptive Epidemiology
Figure 3.5: Measles in Africa
January–June 2010
July–December 2010
Ethiopia
Ethiopia
Somalia
Somalia
Kenya
Kenya
0
250
500
750
1,000 km
January–June 2011
Ethiopia
July–December 2011
Ethiopia
Somalia
Kenya
Somalia
Kenya
Measles Incidence per 1 Million
Integrated Food Security Classification (IPC)
5000
1–4
4 Humanitarian emergency
1,000–4,999
0
5 Famine/Humanitarian catastrophe
50–999
No data
Dadaab refugee camp
5–49
Dollo Ado refugee camp
Source: Adapted from Centers for Disease Control and Prevention. (2012f, August 31). Measles – Horn of Africa, 2010–2011. Morbidity
and Mortality Weekly Report, 61(34), 678–684. Retrieved from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6134a4.htm?s_
cid=mm6134a4_w
How can comparing international variations in health outcomes help researchers identify and develop
potential solutions to health concerns, such as measles outbreaks in Ethiopia, Kenya, and Somalia?
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Section 3.7 Descriptive Epidemiology
CHAPTER 3
Regional Variations Within the United States
Conditions that show regional variations are Lyme disease, obesity, HIV infections, and
many others. Among the causes of these variations are differences in climate, local environmental conditions, socioeconomic status, lifestyle and cultural factors, and availability
of health care services. As an illustration, local physical environmental conditions may be
conducive to the survival of microbes and disease vectors.
A specific example of a health-related phenomenon that varies within the United States is
underage drinking and driving. Every state in the United States prohibits those under the
age of 21 years from driving with any measurable amount of alcohol in their blood; however, many young individuals drink and drive. The Youth Risk Behavior Survey (YRBS)
collects information on the prevalence of drinking and driving among high school students. See Figure 3.6 for 2011 data on the percentage of high school students aged 16 years
and over who had been drinking and driving. The CDC found that
[a]mong the 41 states with available YRBS results in 2011, prevalence of
drinking and driving varied threefold, from 4.6% in Utah to 14.5% in North
Dakota (median: 10.1%). States in the highest tertile included much of the
upper Midwest; the western states of Montana, Wyoming, and New Mexico; South Carolina; and states along the Gulf Coast, except for Florida.
(CDC, 2012g, para. 12)
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Section 3.7 Descriptive Epidemiology
Figure 3.6: Percentage of high school students age 16 and older who had been drinking
and driving, 2011
WA
MT
OR
VT
ND
ME
MN
ID
WY
UT
CA
AZ
CO
PA
IL
KS
OK
NM
MI
IA
NE
NV
OH
IN
WV
MO
VA
KY
NC
TN
AR
SC
MS
TX
NH
MA
RI
CT
NJ
DE
MD
NY
WI
SD
AL
GA
LA
AK
FL
HI
Percentage
11.3–14.5
11.3–14.5
11.3–14.5
11.3–14.5
Source: Adapted from Centers for Disease Control and Prevention. (2012g, October 5). Vital signs: Drinking and driving among high
school students aged ≥16 years—United States, 1991–2011. Morbidity and Mortality Weekly Report, 61(39), 796–800. Retrieved from
http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6139a5.htm?s_cid=mm6139a5_w#fig1
What factors might have caused variations in the rates of drinking and driving between states?
Another illustration of regional variation is differential occurrence of motor vehicle
crashes (MVCs). Although death rates from MVCs have shown an improving trend in
recent years, they continue to be one of the dominant causes of injury and death in the
United States (CDC, 2012h). In general, MVC death rates for MSAs tend to be higher in
southern states, with the highest rates concentrated in the southeastern part of the United
States. See the map shown in Figure 3.7 for death rates during 2009 from MVCs for the
most highly populated MSAs. Of the 50 MSAs, the overall MVC death rate for the 63
major cities combined was 7.9 per 100,000 residents. The CDC reported that
In 2009, MVC death rates for the 50 most populous U.S. metropolitan statistical areas (MSAs) combined were lower than for the nation overall, for
persons of all ages and for those aged 15–24 years. However, MVC death
rates varied widely among MSAs, ranging from 4.4 to 17.8 per 100,000 residents of all ages (age-adjusted) and from 7.3 to 25.8 per 100,000 residents
aged 15–24 years. (2012h, para. 16)
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Section 3.7 Descriptive Epidemiology
Figure 3.7: Motor vehicle crash death rates
WA
MT
OR
VT
ND
ME
MN
ID
WI
SD
WY
NE
NV
UT
CO
IL
KS
CA
MI
IA
PA
IN OH
WV
MO
KY
TN
AZ
NM
OK
AR
LA
VA
NC
SC
MS
TX
NH
MA
RI
CT
NJ
DE
MD
NY
AL
GA
AK
FL
HI
Motor Vehicle Crash Deaths* in Metropolitan Areas — United States, 2009
11.1–17.8
8.6–10.7
6.9–8.4
4.4–6.8
*Per 100,000 population, age-adjusted.
Source: Adapted from Centers for Disease Control and Prevention. (2012h, July 20). Motor vehicle crash deaths in metropolitan areas—
United States, 2009. Morbidity and Mortality Weekly Report, 61(28), 523–528. Retrieved from http://www.cdc.gov/mmwr/preview/
mmwrhtml/mm6128a2.htm?s_cid=mm6128a2_w
Comparing regional variations could uncover information about why certain regions have a higher
incidence of certain outcomes. What might researchers find if they were to compare the differential
occurrence of motor vehicle crashes across the country?
Time (Temporal) Variables
The epidemiologic variable of time includes the following time trends: secular (longterm), seasonal, clustering, and epidemic. Investigations of temporal variation help to
identify changing patterns in morbidity and mortality, demonstrate the success of preventive efforts for chronic and other diseases, and signal the occurrence of epidemics.
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Secular Trends
The term secular trend refers to the gradual changes in disease frequency over long time
periods. For example, mortality due to coronary heart disease in the United States for both
females and males has shown a decreasing secular trend during the past few decades. Figure 3.8 illustrates secular trends in coronary heart disease mortality by sex in the United
States. Between 1999 and 2006, mortality rates per 100,000 persons declined for both males
and females.
Rate per 100,000
Figure 3.8: U.S. secular trends in coronary heart disease mortality by sex
344.6
Male
299.0
Female
253.4
207.8
162.2
116.6
2006
2005
2004
2003
2002
2001
2000
1999
71.0
Year
Source: Adapted from Division for Heart Disease and Stroke Prevention: Data Trends & Maps. (2010). Atlanta, GA: U.S. Department of
Health and Human Services, Centers for Disease Control and Prevention (CDC), National Center for Chronic Disease Prevention and
Health Promotion. Retrieved from http://www.cdc.gov/dhdsp/
Secular trends, such as the rate of coronary disease mortality by sex shown in this graph, illustrate the
changes in disease frequency over long periods.
Cyclic Fluctuations
The phenomenon of cyclic fluctuations reflects increases or decreases in the frequency of
diseases or other health conditions during a year or over a period of several years. Often,
cyclical fluctuations reflect seasonal patterns in the occurrence of disease; these fluctuations reflect the influences of weather, temperature, rainfall, and changes in the characteristics of disease agents and host susceptibility. Seasonal influenza is an example of a
condition that shows cyclic fluctuations, peaking during the winter and declining during
the summer.
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Epidemic Trends
One type of epidemic trend is a point epidemic, which is the response of a group of
people to a common exposure that happened to all people in the group at about the same
time. An epidemic curve is “[a] graphic plotting of the distribution of cases by time of
onset” (Porta, 2008, p. 80). Figure 3.9 demonstrates an epidemic curve for an outbreak of
Salmonella that was associated with live poultry in backyard flocks. Ill persons reported
having had contact with live baby poultry—for example, chickens, ducks, and turkeys.
Individuals who were made ill by Salmonella bacteria had purchased the poultry from a
mail order company; they had intended to use the poultry for eggs and meat or as pets. A
total of 93 persons in 23 states were afflicted; there were 21 hospitalizations and one death.
Figure 3.9: Persons infected with the outbreak strain of Salmonella Montevideo, by
date of illness onset
Number of Persons
5
4
3
2
1
26
p-
12
Se
p-
9
Se
-1
-2
Au
g
18
Au
g
Ju
l-
0
Ju
l-4
-6
3
-2
Ju
n
Ju
n
ay
-2
M
5
ay
-9
M
r-2
Ap
28
r-1
1
Ap
ar-
M
ar14
9
M
-2
Fe
b
Fe
b-
15
0
Date of Illness Onset, 2012
Source: Adapted from Centers for Disease Control and Prevention. (2012). Salmonella. Multistate outbreak of human Salmonella
Montevideo infections linked to live poultry in backyard flocks (final update). Retrieved from http://www.cdc.gov/salmonella/
montevideo-06-12/epi.html
The epidemic curve shown in the graph represents the distribution of onset of Salmonella within a
specific group and time period. What factors may have caused the increased rate of onset between April
12 and May 6?
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Section 3.7 Descriptive Epidemiology
CHAPTER 3
Clustering
The phenomenon of clustering refers to
[a] closely grouped series of events or cases of a disease or other healthrelated phenomena with well-defined distribution patterns in relation to
time or place or both. The term is normally used to describe aggregation
of relatively uncommon events or diseases (e.g., leukemia, multiple sclerosis). (Porta, 2008, p. 43)
Case clustering denotes an unusual aggregation of health events grouped together in time
(temporal clustering) or space (spatial clustering). One illustration of temporal clustering is postvaccination reactions following immunizations; these reactions usually occur
within a number of hours after a vaccination. An example of spatial clustering occurred
in New York City in 1999, when the city experienced an outbreak of the West Nile virus,
a type of virus called a flavivirus; this virus can infect humans, birds, mosquitos, horses,
and some other mammals.
West Nile virus (WNV) is a potentially serious neurological illness. It can infect wild birds
following mosquito bites. Researchers devised an early warning system for WNV outbreaks that used small-area clusters of dead birds in New York City (Mostashari et al.,
2003). They developed a method for detecting dead bird clusters that could differentiate
between normal levels of bird mortality and increases in bird mortality above expected
levels. This information suggested when mortality from WNV was on the increase and
served as a possible harbinger of WNV-carrying mosquitos that could bite humans. Figure 3.10 shows concentrations of dead bird clusters, WNV-positive dead birds, human
cases, and mosquito traps in New York City in 2001. Shading in the figure reveals the
cumulative frequency of dead bird clusters in census tracts.
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CHAPTER 3
Section 3.7 Descriptive Epidemiology
Figure 3.10: Concentrations of dead bird clusters
A
B
June 22
July 22
Bronx
Manhattan
Queens
Retrospective
Brooklyn
Staten
Island
50
0
50 Miles
C
D
September 20
August 21
E
October 31
Census Tract Clusters, 2001
0
Positive human cases
1–2
Positive mosquito traps
3–5
6–9
Positive dead birds
10–14
15+
Source: Adapted from Mostashari, F., Kulldorff, M., Hartman, J. J., Miller, J. R., & Kulasekera, V. (2003, June). Dead bird clusters as an
early warning system for West Nile virus activity. Emerging Infectious Diseases [serial online]. Retrieved from http://wwwnc.cdc.gov/eid/
article/9/6/02-0794-f3.htm
How can clustering help researchers develop early warning systems to alert populations to potential
outbreaks of serious illnesses?
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Section 3.8 Epidemiologic Study Designs Relevant to Community Health
CHAPTER 3
3.8 Epidemiologic Study Designs Relevant to Community Health
Among the major types of study designs of importance to community health are crosssectional studies, cohort studies, and intervention studies. A number of considerations
influence the choice of a study design (e.g., the purpose of the epidemiologic research
[descriptive or analytic]; financial and other resource constraints; speed with which the
results are needed; and frequency of occurrence [prevalence] of particular health outcomes and of exposures in the population).
Cross-Sectional Studies
A cross-sectional study is a type of study design referring to a single period of time in
which exposure and outcome information are collected at the same time. An example of a
cross-sectional study is a community prevalence survey that seeks information about the
frequency of a health issue (say, cigarette smoking), and factors associated with the health
issue. Possible factors associated with tobacco use could be demographic characteristics
and cultural factors stemming from ethnic group membership. Often, demographic factors affect variations in a range of health outcomes as noted in the previous discussion of
descriptive epidemiology.
Case-Control Studies
Case-control studies consider two groups: one in which every member has the disease of
interest (cases) and a comparison group in which every participant is free from the disease (controls). A case-control study attempts to identify possible causes of the disease by
finding out how the two groups differ with respect to a past exposure. To illustrate, a casecontrol study of smoking could compare the smoking histories of lung cancer patients
(the cases) with the corresponding histories of noncases (controls). A case-control study is
a type of retrospective study because information is collected about exposures that have
already occurred. Case-control studies are used extensively in epidemiologic research
for studies of cancer, heart disease, infectious diseases, and many other conditions. An
example of a case-control study is one that examined the effects of pregnant women’s use
of diethylstilbestrol (DES) upon their female fetuses. Between 1938 and 1971, U.S. doctors
prescribed DES to prevent miscarriages and other problems in pregnancy, exposing an
estimated 5–10 million pregnant women and children to DES. In 1953, research showed
that DES did not prevent miscarriages and premature births. Further, in 1971, the FDA
issued a bulletin advising doctors that DES was associated with a rare vaginal cancer in
girls and young women exposed to DES in the womb.
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Section 3.8 Epidemiologic Study Designs Relevant to Community Health
CHAPTER 3
Cohort Studies
Cohort studies are types of longitudinal (often prospective)
studies that trace adverse health
effects associated with specific
exposures. A cohort is defined
as a “population group, or subset thereof (distinguished by a
common characteristic [such as
a specific exposure]), that is followed over a period of time”
(Friis & Sellers, 2009, p. 284).
Because they are able to preserve
the temporal ordering between
Flirt/SuperStock
cause and effect, cohort studies
are among the strongest epide- As smartphones become smaller and more useful, they play a
miologic designs for assessing greater role in everyday activities. In a cohort study involving
causal associations between participants aged 20–24, increased email/chat use was tied to
exposures and health outcomes. an increase in difficulty sleeping.
Temporal ordering of cause and
effect means that the occurrence of the cause is observed before the occurrence of the
effect. This arrangement is not possible with cross-sectional and case-control studies.
Cohort studies begin with groups of participants who lack a positive history of an outcome of interest (e.g., a disease) and who have differing levels of exposures to risk factors for the disease at the initiation of the study. Possible outcome measures include the
incidence of disease and mortality, which are examined for changes that occur as a result
of exposure to a risk factor. Examples of risk factors include smoking, excessive intake of
high caloric foods, insufficient levels of daily physical activity, and inadequate applications of sunscreen.
An example of a published cohort study is one performed by Thomee, Harenstam, and
Hagberg (2012). These researchers examined the association of high computer use (the
exposure or risk-factor variable) with the development of mental health symptoms (outcome variable) among a cohort of 20- to 24-year-old participants. Exposure variables
included the amount of time spent on the computer (i.e., email/chat, computer gaming,
general use, use of the computer without a break, and use of a computer during the night
time causing sleep loss). Outcome variables encompassed mental health states (e.g., perceived stress, sleep disturbances, symptoms of depression, and tiredness; Thomee, Harenstam, & Hagberg, 2012). One of the findings of the study was that high email/chat use
was a factor in sleep disturbance among male participants.
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Section 3.9 Preventing Disease in the Community
CHAPTER 3
Intervention Studies
In a broad sense, there are two types of intervention designs: controlled clinical trials,
which focus on the individual, and community interventions, which focus on the group
or community. A clinical trial is a carefully designed and executed investigation of the
effects of a treatment or technology that uses randomization of participants to study conditions, blinding a subject to study conditions, and manipulation of the study factor. Randomization of subjects involves the use of a computer or similar means to assign subjects
at random in order to prevent study biases. Blinding (also called masking) is a means of
controlling study bias by preventing the subjects (as well as researchers) from knowing
whether the subjects are under the experimental or control conditions of the experiment.
Double-blind studies are experimental procedures in which neither the subjects of the
experiment nor the persons administering the treatment know the critical aspects of the
experiment, in order to control for biases in reporting outcomes or treatment protocols.
Clinical trials are used to test the efficacy of preventive or therapeutic measures such as
vaccines and new medicines.
A community intervention is a preventative effort that aims to change a population’s education and behavior. An example of a community intervention occurred in Colorado. With
funding from the CDC, the Colorado Department of Public Health & Environment’s Oral
Health Unit expanded its community intervention Be Smart & Seal Them! program to provide sealants to all Colorado children at greatest risk for tooth decay. School-based programs offered children in rural and urban areas dental sealants, as well as other modalities
for improved dental health including oral health screenings and treatment plans (CDC,
2008). Prior to this intervention, many of the participating children had never seen a dental provider.
Another example of a community intervention is the Community Trials Intervention to
Reduce High-Risk Drinking (SAMHSA’s National Registry of Evidence-Based Programs
and Practices, 2013). This intervention is a multicomponent, community-based program
that targets alcohol abuse and associated issues among all age groups. The program
uses various environmental interventions such as zoning and municipal regulations that
restrict alcohol access, educational programs for beverage servers and retailers, increased
law enforcement of sobriety rules, reductions in youth access to alcohol, and formation of
community prevention coalitions.
3.9 Preventing Disease in the Community
Once the causes of disease or other adverse health outcomes have been identified, the
next major goal of epidemiology with respect to the health of the community is prevention of these conditions. Community disease prevention programs encompass the three
main levels described below. One of the components related to prevention is screening for
disease in the community.
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Section 3.9 Preventing Disease in the Community
CHAPTER 3
Prevention of Disease
Earlier in the chapter, we learned that epidemiologic studies lead to practical steps to control diseases in the community. Based on identification of risk factors for diseases, individuals and public health departments can introduce preventive measures. One of the
helpful public health/epidemiologic models for prevention of disease proposes three levels of prevention: primary, secondary, and tertiary. Each of the three levels of prevention is
related to phases of the pathogenesis of disease. Pathogenesis ranges from the periods of prepathogenesis (before a diseasecausing agent interacts with a
host), early pathogenesis (early
phases of the disease), and late
pathogenesis (when the patient
recovers, is permanently disabled, or passes away).
Primary prevention attempts to
reduce the occurrence of disease
by preventing disease agents
from interacting with the host.
It is directed to the period of
pre-pathogenesis (CDC, 2007).
Examples of primary prevenB. BOISSONNET/BSIP/BSIP/SuperStock
tion of disease are providing
clean water to the community,
Taking vitamins is an example of a primary prevention strategy.
creating a healthful living environment, and making foods safe
for human consumption. Other examples of primary prevention are general health promotion and risk reduction practices, such as wearing protective devices to prevent occupational injuries, vitamin consumption, immunizations against infectious diseases, and
educational programs for bicycle riders about helmet safety.
Secondary prevention (CDC, 2007) aims to reduce the progression of disease once it has
occurred and is directed to the period of early pathogenesis. The primary goal of secondary prevention is to arrest diseases in their earliest stages once they have infected a human
host and to limit disability from them. Almost all screening programs are forms of secondary prevention. Examples are cancer screening programs (e.g., breast and prostate cancer
screening), diabetes screening, and cardiovascular disease screening.
Tertiary prevention includes efforts to rehabilitate and restore an individual to an optimal functional level. Examples are physical therapy for stroke victims, fitness programs
for heart attack patients, and transitional living arrangements for persons released from a
drug treatment center. Tertiary prevention is directed toward the late stages in pathogenesis of disease.
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CHAPTER 3
Summary
Screening Programs
The process of screening aims to
quickly identify unrecognized
disease or defects through rapidly applied tests, examinations,
or other procedures. The purpose of screening is to prevent
disease or to detect disease early
by identifying persons at a point
in the natural history of disease
when the disease process can be
influenced. Examples of screening tests applied to community
settings are mammography
screening for breast cancer, cervical cancer screening, hypertension screening, and tests for
diabetes.
B BOISSONNET/BSIP/SuperStock
Screening for diabetes is an example of a secondary prevention
strategy.
A number of considerations determine whether a health condition is suitable for screening programs (Wilson & Jungner, 1968). The considerations about whether to screen for
“disease X” are social, scientific, and ethical. The social aspect of screening means that
the health condition must be important for the individual and the community. The scientific consideration means that the natural history of the condition being screened is
understood sufficiently so that early identification of the condition is possible. The ethical
dimension of screening means that it is feasible to alter the natural history of the condition
among those screened.
In some instances, the process of screening for diseases has become controversial. One
of the downsides of screening tests is that they may produce false positive results. As
an example, screening for prostate cancer by measuring PSA (prostate-specific antigen)
has become controversial because positive test results could lead to invasive follow-up
procedures and surgery that result in unnecessary complications. If a cancerous lesion is
detected, slow-growing prostate cancers may be less dangerous than available surgical
and other forms of treatment.
Similarly, screening for breast cancer is also controversial. One issue concerns who should
be screened and the age at which screening for breast cancer should begin. For example,
should screening first be applied to asymptomatic women aged 40 through 49 years, or
should screening be started later in life?
Summary
Many public health experts regard epidemiology as the fundamental discipline of both
public and community health. This chapter has provided an overview of key concepts in
epidemiology and how they relate to the health of the community. We have seen that epidemiology is unique with respect to its concern with the health of a total community and
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Study Questions and Exercises
CHAPTER 3
not merely the health of individuals. The two types of epidemiology are descriptive epidemiology (describing the occurrence of disease) and analytic epidemiology (determining
the causes of disease). We have traced the history of epidemiology from its beginnings in
classical history to contemporary epidemiologic research. We reviewed four of the most
important uses (e.g., studying disease causality) of epidemiology for analyzing community health, and the remainder of the chapter covered data sources and measures used
in community health research, descriptive epidemiology and other epidemiologic study
designs, and how epidemiology contributes to the prevention of disease in the community.
Study Questions and Exercises
1. Define the term “epidemiology.” Provide one example of how epidemiology has
been used to study a health problem in a community.
2. Explain why epidemiology is important for the control and prevention of health
problems in communities.
3. In 2009, the leading cause of death in the United States was heart disease. Compare and contrast how an epidemiologist and a clinician would focus on this
problem.
4. In your own words, explain the difference between descriptive and analytic
epidemiology.
5. The definition of descriptive epidemiology includes the use of “person,” “place,”
and “time.” Provide one example for each variable in relationship to a health
outcome.
6. Define “prevalence” and “incidence.” Explain the difference between these two
terms.
7. Which descriptive study design is useful for studying the prevalence of a health
problem in a community?
8. Describe the key features of the following study designs: case-control and cohort.
9. An important feature of clinical trials involves randomization. What is randomization and why is it an important aspect of clinical trials?
10. The three levels of prevention are primary, secondary, and tertiary. For each
description listed below, identify the level of prevention.
a. A community mental health nurse conducting a workshop on suicide to the
local Parent–Teacher Association
b. A doctor checking a patient for suspicious skin growths
c. A community-wide educational campaign about the importance of not texting
while driving
d. A father initiating dietary changes to reduce his blood cholesterol levels
e. A nurse teaching a recently diagnosed diabetic how to identify and prevent
complications
f. A health educator teaching college students the correct way to use a condom
g. A doctor referring a stroke patient to a physical therapy program
h. A local health department offering free seasonal influenza vaccines to the
community
i. A young woman visiting the local clinic for an annual pelvic exam and Pap
smear
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Key Terms
CHAPTER 3
Key Terms
analytic epidemiology The process of
using data (including descriptive epidemiological information) to study patterns
that suggest potential causes of or associations between diseases and other health
conditions.
blinding A means of controlling study
bias by preventing researchers and subjects
from knowing whether the subjects are
part of the experimental or control conditions; also known as masking.
case fatality rate The number of deaths
caused by disease among those who have
the disease during a specific time period.
clinical trial A carefully designed and
executed investigation of the effects of a
treatment or technology that uses randomization of participants to study conditions.
clustering The grouping of health-related
events or diseases that are related both in
proximity and temporally.
cohort A population, group, or subset of
a population/group that is studied over a
period of time.
count The total number of cases of a disease or other health phenomenon.
cyclic fluctuations Increases or decreases
in the frequency of diseases or other health
conditions during a year or over a period
of several years.
descriptive epidemiology The characterization of disease occurrence in populations according to classification by person,
place, and time (includes factors such as
age, race, gender, and educational level).
epidemic curve A graphic plot of the distribution of disease cases by onset time.
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epidemiology The study of incidence,
distribution, and control of diseases.
exposure The potential causal factor in an
epidemiological study.
incidence The number of new cases of a
disease or condition within a specific time
period.
incidence rate Describes the rate of development of a disease within a group during
a specific time period.
mortality rate Death rate; the number of
deaths during a given year divided by the
size of the population during the midpoint
of the year.
point epidemic The response of a group
of people to a common exposure that happened to all people in the group at about
the same time.
point prevalence The frequency of disease or other condition at a given point
in time.
prevalence A measure of disease occurrence (i.e. the total number of cases).
primary prevention A means of preventing a disease or condition from occurring.
proportion A type of ratio in which
the numerator is part of the denominator; indicates a part of a whole, such as a
percentage.
rate A measure that includes time as part
of the denominator; in public health, the
numerator is typically the frequency of
disease occurrence, and the denominator is
typically the unit size of the population.
ratio A value obtained by dividing one
quantity by another; examples include
rate, proportion, and percentage.
9/16/13 4:18 PM
Key Terms
registry A centralized database for collection of information about a particular
disease.
screening The presumptive identification
of unrecognized disease or defects by the
application of tests, examinations, or other
procedures that can be applied rapidly.
CHAPTER 3
secular trends Changes in disease
frequency.
tertiary prevention A means of rehabilitating or restoring an individual to optimal
functioning; it is directed toward the late
stages in pathogenesis of disease.
secondary prevention A means of reducing the progression of a disease or condition once it has occurred.
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Chapter 4
Exactostock/SuperStock
Epidemiology of Infectious Diseases
Learning Objectives
After reading this chapter, you should be able to:
•• Define the term infectious disease.
•• Explain the significance of infectious diseases for community health.
•• Describe modes of transmission of infectious diseases.
•• Apply the epidemiologic triangle to at least three infectious diseases.
•• State two methods for the prevention of infectious disease outbreaks.
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CHAPTER 4
Section 4.1 Introduction
4.1 Introduction
Infectious diseases are a major focus of epidemiology and community health. Globally,
infectious and parasitic diseases account for a significant proportion of deaths. In 2008, the
World Health Organization estimated that about 15% (8.5 million) of a total of 56.9 million deaths globally were associated with infectious and parasitic conditions. The leading
infectious killers and their respective numbers of deaths in millions were tuberculosis (1.3),
human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) (1.8),
and malaria (0.8). Moreover, childhood infectious diseases such as whooping cough and
measles were among the leading causes of death among the world’s children (WHO, 2011).
Great strides have been made in the control of infectious diseases. By the mid-20th century, some public health experts believed that the tide of mortality and morbidity from
infectious diseases, especially those caused by bacteria, had been reduced and that infectious diseases might even be eradicated by newly introduced antibiotics and other “magic
bullets.” Today, few public health experts hold this opinion, as microbes have developed
resistance to antibiotics, causing the reemergence of once-controlled infectious diseases.
Conditions known as “emerging infections” have gained a foothold worldwide and continue to endanger the population.
In the United States, infectious diseases continue to be major causes of mortality, although
chronic noncommunicable diseases such as heart disease, cancer, and chronic respiratory
disease form the top tier of leading causes of death. The category of pneumonia and influenza was the eighth leading cause in 2009 (CDC, 2013a).
The advent of an increasingly international world is one of the influences that could lead
to epidemics of infectious diseases. Modern jet travel means that people can travel rapidly from one distant area to another. Nonendemic diseases, such as malaria, may be imported
into the United States by travelers returning from
areas in which these conditions are endemic. A
dangerous communicable disease that has broken out in a remote corner of the globe can be
introduced within a 24-hour period to a major
city in Asia or Europe. From these continents,
the disease can spread to the United States. As an
extreme fictional example, the 2011 medical disaster film Contagion portrayed the epidemic spread
of a lethal virus transmitted by fomites (inanimate
objects such as skin or hair), attempts by epidemiologists and public health officials to identify and
contain diseases, the loss of a social order due to
the pandemic, and the introduction of a vaccine to
halt the disease’s spread.
Along with the trend of rapid international travel,
major growth in international trade of food and
medicines during the current century can also
contribute to the spread of diseases. Improperly
processed foods and medicines can bypass normal
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Exactostock/SuperStock
Modern technological advances such as
air travel could contribute to the quick,
international spread of infectious disease.
8/30/13 1:08 PM
CHAPTER 4
Section 4.2 Microbes in History
inspection and safety procedures, especially when the volume of such products exceeds
the capacity of health authorities.
An additional possible factor linked with the spread of infectious diseases is deforestation
in many parts of the world. Deforestation causes the destruction of natural ecosystems
that are important for human survival. Among the consequences of ecosystem loss is the
potential for increases in runoff of pollutants into human water supplies and changes in
the distribution of disease-causing vectors. Growing urbanization and pressures from the
human population explosion cause people to move into previously forested areas, where
they may come into contact with new vectors and infectious disease agents.
This chapter provides an overview of infectious diseases and their impact on the health
of communities. Topics covered include the discovery of disease-causing microbes, the
epidemiologic triangle (agent, host, and environment), examples of significant infectious
diseases, and methods for the prevention of communicable diseases.
4.2 Microbes in History
Chapter 1 described historical accounts of dramatic epidemics that caused devastating effects. Examples of historically significant epidemics were the Black Death (caused
by Yersenia pestis), recurring smallpox epidemics, and pandemic influenza. The Black
Death ravaged the populations of Europe, northern Africa, and the Middle East from 1346
to 1352 (McEvedy, 1988). In 1346, the Black Death killed 20 million people in Europe.
The 1918–1919 Spanish flu pandemic was responsible for 50 million deaths worldwide
(Taubenberger & Morens, 2006).
Before the late 19th century, the causes of infectious diseases were largely unknown. Gradually, during the mid-19th century, disease detectives became aware of microbes as causes
of human diseases and developed the germ theory of disease. The germ theory of disease
arose from the discovery that certain infectious diseases were caused by microorganisms
that invade the body. Microorganisms are small organisms that cannot be seen without
magnification. This germ theory of disease contributed to an understanding of the function that microbial agents perform in causing the spread of infectious disease. Eventually
these new insights gained the attention of the public health community and led to the
development of disease prevention efforts.
Among the scientists who stood out in history as instrumental in identifying causative
microbes for infectious diseases were Ignaz Semmelweiss in 1840 and Robert Koch in
1847. Other breakthroughs in the control of infectious diseases included Edward Jenner’s
development of a vaccine against smallpox and Jonas Salk’s formulation of a polio immunization in 1953. These innovations lead to the eradication of many formerly common
epidemic diseases worldwide. Smallpox, the ancient scourge of humanity, was declared
eradicated in 1979. Polio has a very limited presence in the United States, and does not
exist in epidemic form in most parts of the globe.
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CHAPTER 4
Section 4.2 Microbes in History
Ignaz Semmelweiss
Ignaz Semmelweiss was a clinical assistant in
obstetrics and gynecology at a Vienna hospital in
the mid-19th century. According to Friis and Sellers (2009, p. 35),
Silvio Fiore/SuperStock
Ignaz Semmelweiss (1745–1822) contributed
to the germ theory of disease in 1840 with
his theory that hand contamination might
be responsible for the spread of disease.
Semmelweiss’s hypothesis led to hand
washing and, therefore, a decreased spread
of infection in Viennese hospitals.
In 1840, he noticed a much higher
mortality rate of puerperal fever
(childbirth fever) among the women
who were located in the teaching
quarters for medical students and
physicians than in the teaching areas
for midwives. He hypothesized that
medical students and physicians
had contaminated their hands during autopsies, and consequently
transmitted in...
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