Chapter 3 Measures of Morbidity and Mortality Used in Epidemiology Learning Objectives Define and distinguish among ratios, proportions, and rates Explain the term population at risk Identify and calculate commonly used rates for morbidity, mortality, and natality State the meanings and applications of incidence rates and prevalence Learning Objectives (cont’d) Discuss limitations of crude rates and alternative measures for crude rates Apply direct and indirect methods to adjust rates List situations where direct and indirect adjustment should be used Overview of Epidemiologic Measures Count The simplest and most frequently performed quantitative measure in epidemiology. Refers to the number of cases of a disease or other health phenomenon being studied. Examples of Counts Cases of influenza reported in Westchester County, New York, during January of a particular year. Traffic fatalities in Manhattan in a 24-hour time period College dorm students who had mono Foreign-born stomach cancer patients Ratio The value obtained by dividing one quantity by another. Consists of a numerator and a denominator. The most general form has no specified relationship between numerator and denominator. Rates, proportions, and percentages are also ratios. Example of a Simple Sex Ratio Calculation A ratio may be expressed at = X/Y Simple sex ratio (data from textbook) Of 1,000 motorcycle fatalities, 950 victims are men and 50 are women. Number of male cases 950 Number of female cases 50 19:1 male to female = = Example of a Demographic Sex Ratio Calculation This ratio refers to the number of males per 100 females. In the U.S., the sex ratio in 2010 for the entire population was 96.7, indicating more females than males. Number of male cases 151,781,326 Number of female cases 156,964,212 96.7 X 100 = = X 100 Example of a Sex Ratio at Birth Calculation The sex ratio at birth is defined as: (the number of male births divided by the number of female births) multiplied by 1,000. Number of male births Number of female births X 1,000 Definition of Proportion A measure that states a count relative to the size of the group. A ratio in which the numerator is part of the denominator. May be expressed as a percentage. Uses of Proportions Can demonstrate the magnitude of a problem. Example: 10 dormitory students develop hepatitis. How important is this problem? If only 20 students live in the dorm, 50% are ill. If 500 students live in the dorm, 2% are ill. Example of a Proportion Calculate the proportion of African-American male deaths among African-American and white boys aged 5 to 14 years. Rate Definition: a ratio that consists of a numerator and a denominator and in which time forms part of the denominator. Contains the following elements: disease frequency unit size of population time period during which an event occurs Crude death rate = Number of deaths in a given y ...

1. Chapter 3
Measures of Morbidity and Mortality Used in Epidemiology
Learning Objectives
Define and distinguish among ratios, proportions, and rates
Explain the term population at risk
Identify and calculate commonly used rates for morbidity,
mortality, and natality
State the meanings and applications of incidence rates and
prevalence
Learning Objectives (cont’d)
Discuss limitations of crude rates and alternative measures for
crude rates
Apply direct and indirect methods to adjust rates
List situations where direct and indirect adjustment should be
used
Overview of Epidemiologic Measures
Count
The simplest and most frequently performed quantitative
measure in epidemiology.
Refers to the number of cases of a disease or other health
phenomenon being studied.
Examples of Counts

2. Cases of influenza reported in Westchester County, New York,
during January of a particular year.
Traffic fatalities in Manhattan in a 24-hour time period
College dorm students who had mono
Foreign-born stomach cancer patients
Ratio
The value obtained by dividing one quantity by another.
Consists of a numerator and a denominator.
The most general form has no specified relationship between
numerator and denominator.
Rates, proportions, and percentages are also ratios.
Example of a
Simple Sex Ratio Calculation
A ratio may be expressed at = X/Y
Simple sex ratio (data from textbook)
Of 1,000 motorcycle fatalities, 950 victims are men and 50 are
women.
Number of male cases 950
Number of female cases 50
19:1 male to female
=
=
Example of a
Demographic Sex Ratio Calculation
This ratio refers to the number of males per 100 females. In the

3. U.S., the sex ratio in 2010 for the entire population was 96.7,
indicating more females than males.
Number of male cases 151,781,326
Number of female cases 156,964,212
96.7
X 100 =
=
X 100
Example of a
Sex Ratio at Birth Calculation
The sex ratio at birth is defined as: (the number of male births
divided by the number of female births) multiplied by 1,000.
Number of male births
Number of female births
X 1,000
Definition of Proportion
A measure that states a count relative to the size of the group.
A ratio in which the numerator is part of the denominator.
May be expressed as a percentage.

4. Uses of Proportions
Can demonstrate the magnitude of a problem.
Example: 10 dormitory students develop hepatitis. How
important is this problem?
If only 20 students live in the dorm, 50% are ill.
If 500 students live in the dorm, 2% are ill.
Example of a Proportion
Calculate the proportion of African-American male deaths
among African-American and white boys aged 5 to 14 years.
Rate
Definition: a ratio that consists of a numerator and a
denominator and in which time forms part of the denominator.
Contains the following elements:
disease frequency
unit size of population
time period during which an event occurs
Crude death rate =
Number of deaths in a given year
Reference population
(during the midpoint of the year
X 100,000
Example:
Number of deaths in the United States during 2007 =
2,423,712
Population of the U.S. as of July 1, 2007 = 301,621,157
2,423,712

5. 301,621,157
Crude death rate =
= 803.6 per 100,000
Example of Rate Calculation
Definition of Prevalence
The number of existing cases of a disease or health condition in
a population at some designated time.
Figure 3-1: Analogy of prevalence and incidence. The water
flowing down the waterfall symbolizes incidence and water
collecting in the pool at the base symbolizes prevalence.
Source: Robert Friis.
Interpretation of Prevalence
Provides an indication of the extent of a health problem.
Example 1: Prevalence of diarrhea in a children’s camp on July
13 was 15.
Example 2: prevalence of obesity among women aged 55-69
years was 367 per 1,000.
Uses of Prevalence
Describing the burden of a health problem in a population.
Estimating the frequency of an exposure.
Determining allocation of health resources such as facilities and
personnel.
Point Prevalence
Point Prevalence =

6. Number of persons ill
Total number in the group
at point in time
Example:
Total number of smokers in the group = 6,234
Total number in the group 41,837
or 14.9%
= 149.0 per 1,000
Period Prevalence =
during a time period
Period Prevalence
Number of persons ill
Average population
Example:
Persons ever diagnosed with cancer = 2,293
Average population 41,837
= 5.5%
Definition of Incidence
The number of new cases of a disease that occur in a group
during a certain time period.
Incidence Rate (Cumulative Incidence)
Describes the rate of development of a disease in a group over a
certain time period.

7. Contains three elements:
Numerator = the number of new cases.
Denominator = the population at risk.
Time = the period during which the cases occur.
Example of Incidence Data
Number of new cases of HIV infection diagnosed in a
population in a given year: a total of 164 HIV diagnoses were
reported among American Indians or Alaska natives in the U.S.
during 2009.
Incidence Rate Calculation (IWHS Data)
Incidence rate =
Number of new cases
over a time period
Total population at risk
during the same time period
X multiplier (e.g., 100,000)
Number of new cases = 1,085
Population at risk = 37,105
Incidence rate =
1,085
37,105
= 0.02924/8 = 0.003655 x 100,000
= 365.5 cases per 100,000 women per year
Attack Rate (AR)
Alternative form of incidence rate.
Used for diseases observed in a population for a short time
period.
Not a true rate because time dimension often uncertain.
Example: Salmonella gastroenteritis outbreak

8. Formula:
Ill
Ill + Well
AR =
x 100 (during a time period)
Incidence Density
An incidence measure used when members of a population or
study group are under observation for different lengths of time.
Number of new cases during the time period
Total person-time of observation
Incidence density =
Number of new cases during the time period
Total person-years of observation
Incidence density =
If period of observation is measured in years, formula becomes:
Formulas for Incidence Density
Incidence Density, Example
Interrelationship Between Prevalence and Incidence

9. Interrelationship Between Prevalence and Incidence (cont’d)
If duration of disease is short and incidence is high, prevalence
becomes similar to incidence.
Short duration--cases recover rapidly or are fatal.
Example: common cold
Interrelationship Between Prevalence and Incidence (cont’d)
If duration of disease is long and incidence is low, prevalence
increases greatly relative to incidence.
Example: HIV/AIDS prevalence
‹#›
Crude Rates, Measures of Natality
Crude birth rate
Fertility rate
General
Total
Infant mortality rate
Fetal death rate
Neonatal mortality rate
Postneonatal mortality rate

10. Perinatal mortality rate
Maternal mortality rate
Crude Birth Rate
Crude Birth Rate =
Number of live births
within a given period
Population size at the
middle of that period
X 1,000 population
Sample calculation: 4,130,665 babies were born in the U.S.
during 2009, when the U.S. population was 307,006,550. The
birth rate was
4,130,665/307,006,550 = 13.5 per 1,000.
Used to project population changes; it is affected by the number
and age composition of women of childbearing age
General Fertility Rate
General
fertility rate
=
# of live births
within a year
# of women
aged 15-44 yrs.
during the midpoint
of the year
X
1,000 women

11. aged 15-44
Sample calculation: During 2009, there were 61,948,144 women
aged 15 to 44 in the U.S. There were 4,130,665 live births. The
general fertility rate was 4,130,665/61,948,144 = 66.7 per 1,000
women aged 15 to 44.
Used for comparisons of fertility among age, racial, and
socioeconomic groups.
Total Fertility Rate
This rate is “[t]he average number of children that would be
born if all women lived to the end of their childbearing years
and bore children according to a given set of age-specific
fertility rates.”
In the United States, the total fertility rate was 2.06 in 2012.
This rate is close to
The replacement fertility rate is 2.1.
Fetal Death Rate
Used to estimate the risk of death of the fetus associated with
the stages of gestation.
Fetal Death Ratio
Refers to the number of fetal deaths after gestation of 20 weeks
or more divided by the number of live births during a year.
Fetal Death Ratio =
Number of fetal deaths after 20 weeks or more gestation
Number of live births
X 1,000
(during a year)

12. Infant Mortality Rate
Used for international comparisons; a high rate indicates unmet
health needs and poor environmental conditions.
Neonatal Mortality Rate
Reflects events happening after birth, primarily:
Congenital malformations
Prematurity (birth before gestation week 28)
Neonatal Mortality Rate Formula
Postneonatal Mortality Rate
Measures risk of dying among older infants during a given year.
Perinatal Mortality Rate
Reflects environmental events that occur during pregnancy and
after birth; it combines mortality during the prenatal and
postnatal periods.
Perinatal Mortality Ratio

13. Maternal Mortality Rate
Reflects health care access and socioeconomic factors; it
includes maternal deaths resulting from causes associated with
puerperium (period after childbirth), eclampsia, and
hemorrhage.
Crude Rates
Use crude rates with caution when comparing disease
frequencies between populations.
Observed differences in crude rates may be the result of
systematic factors (e.g., sex or age distributions) within the
population rather than true variation in rates.
Specific Rates
Specific rates refer to a particular subgroup of the population
defined in terms of race, age, sex, or single cause of death or
illness.
Cause-Specific Rate
Cause-specific mortality rate (age group 25-34) due to HIV in
2003 = 1,588/39,872,598 = 4.0 per 100,000
Example:
Proportional Mortality Ratio (PMR) %

14. PMR (%) for HIV among the 25- to 34-year-old group =
1,588/41,300 = 3.8%
Indicates relative importance of a specific cause of death; not a
measure of the risk of dying of a particular cause.
Example:
Age-Specific Rate (Ri)
Method for Calculation of Age-Specific Death Rates
Adjusted Rates
Summary measures of the rate of morbidity and mortality in a
population in which statistical procedures have been applied to
remove the effect of differences in compositio n of various
populations.
Direct Method
The direct method may be used if age-specific death rates in a
population to be standardized are known and a suitable standard
population is available.
New Standard Population
Year 2000 population
Replaces the standard based on 1940 population
Results in age-adjusted death rates that are much larger
Affects trends in age-adjusted rates for certain causes of death
Narrows race differentials in age-adjusted death rates

15. Reduces the three different standards into one acceptable
standard
Direct Method for Adjustment of Rates
Weighted Method for Direct Rate Adjustment
Indirect Method
Indirect method may be used if age-specific death rates of the
population for standardization are unknown or unstable, for
example, because the rates to be standardized are based on a
small population.
The standardized mortality ratio (SMR) can be used to evaluate
the results of the indirect method.
Standardized Mortality Ratio
(SMR)
Interpretation of SMR
If the observed and expected numbers are the same, the SMR
would be 1.0, indicating that observed mortality is not unusual.
An SMR of 2.0 means that the death rate in the study population
is two times greater than expected.

16. Indirect Age Adjustment (cont’d)
From previous table, SMR is (502/987.9) X 100 = 50.8%.
Running head: SOCIAL PROBLEM THAT IMPACT BROADLY
1
SOCIAL PROBLEM THAT IMPACT BROADLY
4
Tenea Lewis
SOCW 6351
Social Problem that Impact Broadly
Poverty
March 22,2021

17. Social Problem that Impact Broadly: “Poverty”
As a social worker, I am working on a social problem that has a
broad impact on the population of the world that is "pover ty."
Poverty is a position or situation in which people or society has
no or not enough financial support and essentials for the least
living standard. Poverty indicates that the level of earning of a
poor person from a job is very low that fundamental personal
requirements can't be sufficed.
Poverty is a social problem that has a huge impact on many
people of the world. Poverty has disastrous outcomes for many
people who exist in it and out. Not only do poor people suffer
from the outcomes of poverty but it affects society overall by
contributing to many other social problems. Many studies
carried and interpreted by scientists, nonprofit groups, and
government bureaus have recognized the consequences of
poverty on the lives of people around the world (Lindsey, 2009;
Moore, et. al., 2009; Ratcliffe & McKernan, 2010; Sanders,
2011). In common, poor kids are likely to be weak as grown-
ups, and likely to hold problems in employment, likely to be a
parent in teenage, and likely to leave high school early. Several
of those studies concentrate on the poverty of adolescence, and
certain considerations present this pretty obvious that poverty
of adolescence has permanent outcomes and this can affect the
nation overall. Although 32 % of poor kids grow poor as grown-
ups, just 1 % of kids who are not poor grow up being poor as an
adult (Ratcliffe & McKernan, 2010). This study shows that most
poor kids will be poor for the rest of their life and make a poor
and weak nation.

18. We address a few of the main outcomes of poverty below that
show how’s poverty makes more problems in the society that
has an impact on a huge population or whole nation.
The first problem is family problems that will be occurred
because of that poverty. If there are more family problems in
the society then there will be more psychological problems
occurred in the young people and affects the overall nation.
Poor people are at higher risk for relationship dilemmas,
including domestic violence and divorce. The main purpose for
several of the difficulties families encounter is anxiety. These
anxieties can transfer to the households that even are not poor.
Managing these families can create anxiety, giving the bills can
create anxiety, and kids can create anxiety, and this stress can
lead to crimes, deads, violence, etc. in society and the whole
society could suffer. Poor families have higher anxiety due to
their scarcity, and the normal anxieties of people's existence
become more severe in poor households. The different sorts of
domestic issues, therefore, appear more generally in poor
households than in richer households. Intensifying this
condition, when certain issues happen, poor households have
fewer resources than richer households to handle these
obstacles, and that starts transferring in the society.
The second problem includes health problems, illness, lack of
medical care, etc. that occurred because of more poverty in the
society. Poor people are likely to own many sorts of health
issues that make another part of society ill too. Poor kids are
further likely to get a poor diet and, partially, and due to this
reason nation suffer behavioral, health, and cognitive issues.
Poor people have health issues like earlier adulthood death,
child death, and mental disorder, and they are similarly more
likely to get poor medical attention. Certain issues also reduce
their strength to perform great in school and settle well -built
jobs as grown-ups, serving to guarantee that poverty will
continue beyond generations. As per the recent study, that case
suggests that poverty is accountable for approximately 150,000
demises yearly; a number is equivalent to the deaths amount

19. from cancer of lungs in the year (Bakalar, 2011). This shows
how dangerous poverty can be for the overall future of the
nation.
The third social problem is homelessness that causes
economical and behavioral differences in the nation. Several
poor households spend much as compared to their earnings on
home rentals. Several poor people manage to survive in poor
areas that have no employment possibilities, high-grade schools,
and different characteristics of functional behavior that more
prosperous individuals take for granted. More dangerous is
homelessness, this also causes economical and behavioral
differences all over the population. An approximated 1.6
million individuals that include about 300,000 kids are
homeless (Lee, et. al., 2010). The absence of sufficient
accommodations for the poor continues a significant problem of
a nation because this causes economical and behavioral
differences.
The fourth problem that poverty creates is crimes in the country
and this affects all the population of the nation or world. Poor
people judge for the majority of the crime such as robbery,
burglary, homicide, etc., and poor people further consider for
the majority of sufferers of these crimes. There is a relationship
between street crime and poverty, although they incorporate the
intense disappointment and anxiety of being in scarcity. In these
areas, kids are likely to grow up below the impact of adult
companions who are previously in gangs or unless doing crimes
and many people become victims of these crimes. It is the
reality that several poor people reside in crime focus areas.
Furthermore, the poor people are likely to perform crimes, they
further include most of the individuals imprisoned for crimes,
condemned of crimes, and jailed for crimes. Presently, many of
around 2 million individuals are in the cells and jails of the
nation. Wrongful behavior and unlawful victimization are
different main outcomes of poverty. We can solve these
problems that are created because of the poverty in the nation
by taking few steps. According to the sociologist, John Iceland

20. (Iceland, 2006) stated, “countries regularly spend massively in
both comprehensive benefits, like medical care, child care, and
maternity leave, also in supporting employment among poor
households can solve poverty and its consequences.
References
Lindsey, D. (2009). Child poverty and inequality: Securing a
better future for America’s children. New York, NY: Oxford
University Press.
Moore, K. A., Redd, Z., Burkhauser, M., Mbawa, K., & Collins,
A. (2009). Children in poverty: Trends, consequences, and
policy options. Washington, DC: Child Trends. Retrieved
from http://www.childtrends.org/Files//Child_Trends-
2009_04_07_RB_ChildreninPoverty.pdf
Ratcliffe, C., & McKernan, S.-M. (2010). Childhood poverty
persistence: Facts and consequences. Washington, DC: Urban
Institute Press.
Sanders, L. (2011). Neuroscience exposes pernicious effects of
poverty. Science News, 179(3), 32.
Bakalar, N. (2011, July 4). Researchers link deaths to social
ills. New York Times, p. D5.
Chapter 4
Descriptive Epidemiology: Person, Place, Time
Learning Objectives
State primary objectives of descriptive epidemiology
Provide examples of descriptive studies
List characteristics of person, place, and time
Characterize the differences between descriptive and analytic
epidemiology

21. Descriptive vs. Analytic Epidemiology
Descriptive studies--used to identify a health problem that may
exist. Characterize the amount and distribution of disease
Analytic studies--follow descriptive studies, and are used to
identify the cause of the health problem
2
Objectives of Descriptive Epidemiology
To evaluate and compare trends in health and disease
To provide a basis for planning, provision, and evaluation of
health services
To identify problems for analytic studies (creation of
hypotheses)
3
Descriptive Studies and Epidemiologic Hypotheses
Hypotheses--theories tested by gathering facts that lead to their

22. acceptance or rejection
Three types:
Positive declaration (research hypothesis)
Negative declaration (null hypothesis)
Implicit question (e.g., to study association between infant
mortality and region)
4
Mill’s Canons of Inductive Reasoning
The method of difference--all the factors in two or more places
are the same except for a single factor.
The method of agreement--a single factor is common to a
variety of settings. Example: air pollution.
5
Mill’s Canons (cont’d)
The method of concomitant variation--the frequency of disease
varies according to the potency of a factor.
The method of residues--involves subtracting potential causal
factors to determine which factor(s) has the greatest impact.

23. 6
Method of Analogy
(MacMahon and Pugh)
The mode of transmission and symptoms of a disease of
unknown etiology bear a pattern similar to that of a known
disease.
This information suggests similar etiologies for both diseases.
Three Approaches to Descriptive Epidemiology
Case reports--simplest category of descriptive epidemiology
Case series
Cross-sectional studies
Case Reports and Case Series
Case reports--astute clinical observations of unusual cases of
disease
Example: a single occurrence of methylene chloride poisoning
Case series--a summary of the characteristics of a consecutive
listing of patients from one or more major clinical
Example: five cases of hantavirus pulmonary syndrome
7

24. Cross-sectional Studies
Surveys of the population to estimate the prevalence of a
disease or exposure
Example: National Health Interview Survey
Characteristics of Persons Covered in Chapter 4
Age
Sex
Marital Status
Race and ethnicity
Nativity and migration
Religion
Socioeconomic status
Age
One of the most important factors to consider when describing
the occurrence of any disease or illness
8
Trends by Age Subgroup
Childhood to early adolescence

25. Leading cause of death, ages 1-14 years—unintentional injuries
Infants—mortality from developmental problems, e.g.,
congenital birth defects
Childhood—occurrence of infectious diseases such as
meningococcal disease
Trends by Age Subgroup (cont’d)
Teenage years
Leading causes of death—unintentional injuries, homicide, and
suicide
Other issues—unplanned pregnancy, tobacco use, substance
abuse
Trends by Age Subgroup (cont’d)
Adults—leading causes of death
Unintentional injuries
Cancer
Heart disease
Older adults—deaths from chronic diseases (e.g., cancer and
heart disease) dominate.
Elderly—deaths from chronic diseases and limitations in
activities of daily living
Age Trends in Cancer Incidence
Age-specific rates of cancer incidence increase with age with
apparent declines late in life.

26. Reasons for Age Associations
Validity of diagnoses across the life span
Multimodality of trends
Latency effects
Action of the “human biologic clock”
Life cycle and behavioral phenomena
Validity of Diagnoses
Classification errors
Age-specific incidence rates among older groups
Exact cause of death can be inaccurate due multiple sources of
morbidity that affect elderly.
Age-Specific Distributions of Disease Incidence
Age-specific distributions of disease incidence can be linear or
multimodal.
Linear trend—incidence of cancer
Multimodal (having several peaks in incidence)
Tuberculosis—peaks at ages 0 to 4 and ages 20-29
Meningococcal disease—peaks among infants younger than age
1 year and teenagers about 18 years old
Latency Effects
Age effects on mortality may reflect the long latency period

27. between environmental exposures and subsequent development
of disease.
Biologic Clock Phenomenon
Waning of the immune system may result in increased
susceptibility to disease, or aging may trigger appearance of
conditions believed to have genetic basis.
Example: Alzheimer’s disease
9
Sex Differences: Males
All-cause age-specific mortality rates is higher for men than for
women.
May be due to social factors
May have biological basis
Men often develop severe forms of chronic disease.
Generally, death rates for both sexes are declining.
10

28. Sex Differences: Female Paradox
Reports from the 1970s indicated female age-standardized
morbidity rates for many acute and chronic conditions were
higher than rates for males, even though mortality was higher
among males.
Higher female rates for:
Pain
Asthma
Some lung difficulties
Cancer
Cancer of the lung and bronchus is leading cause of cancer
death for both men and women in the U.S.
Increases among women are related to changes in lifestyle and
risk behavior, e.g., smoking.
CHD among Women
Coronary heart disease (CHD) is the leading cause of mortality
among women (and also men).
Women may not be alert for symptoms of CHD and fail to seek
needed treatment.
Minority Women in Economically Disadvantaged U.S. Areas
In Los Angeles County, some have higher rates of diabetes and
hypertension than men.
A large percentage are physically inactive.

29. High rates of obesity among Latinas and African Americans.
Marital Status
Categories
Single or non-married (e.g., never married, divorced, widowed)
Married
Living with a partner
Marital Status (cont’d)
In general, married people tend to have lower rates of morbidity
and mortality.
Examples: chronic and infectious diseases, suicides, and
accidents.
Never married adults (especially men) less likely to be
overweight
11
Marital Status (cont’d)
Marriage may operate as a protective or selective factor.
Protective hypothesis: marriage provides an environment
conducive to health.
Selective hypothesis: people who marry are healthier than
people who never marry.
Marital Status (cont’d)

30. Widowed persons
Suicide rates
Elevated among young white males who were widowed
Depression
Elevated rates among widowed persons
General Comments About Race
U.S. is becoming increasingly more diverse.
Race is an ambiguous concept that overlaps with other
dimensions.
Some scientists propose that race is primarily a social and
cultural construct.
Measurement of Race
Census 2000 changed the race category by allowing respondents
to choose one or more race categories.
Census 2000 used five categories of race.
Census 2010 continued with this classification scheme (Refer to
Exhibit 4-1 in text).
Race/Ethnicity Categories Discussed in Chapter 4
African American
American Indian
Asian
Hispanic/Latino
African Americans
In a classic study of differential mortality in U.S., they had the
highest rate of mortality of all groups studied.

31. Higher blood pressure levels
Possible influence of stress or diet.
Higher rates of hypertensive heart disease.
In 2007, age-adjusted death rate for African Americans was 1.3
times rate for whites.
Differences in life expectancy
12
American Indians/Alaska Natives
High rates of chronic diseases, adverse birth outcomes, and
some infectious diseases
Pima Indians (1975-1984 data):
High mortality, e.g., male death rate (ages 25 to 34) was 6.6
times that for all races in U.S.
Infectious diseases were the 10th leading cause of death.
13

32. Asians
Japanese Americans have lower mortality rates than whites.
Lower rates of CHD and cancer.
Low CHD rates attributed to low-fat diet and institutionalized
stress-reducing strategies.
Some Asian groups, e.g., Cambodian Americans, have high
smoking rates.
TB rates are highest among Asian/Pacific Islander group.
14
Acculturation
Defined as modifications that individuals or groups undergo
when they come in contact with another country
Provides evidence of the influence of environmental and
behavioral factors on chronic disease
Example: Japanese migrants experience a shift in rates of
chronic disease toward those of the host country.
15

33. Hispanics/Latinos
Hispanic Health and Nutrition Examination Survey (HHANES).
Examined health and nutrition status of major Hispanic/Latino
populations in the U.S.
San Antonio Heart Study
Found high rates of obesity and diabetes among Mexican
Americans
Hispanic mortality paradox (text box)
16
Nativity and Migration
Nativity--Place of origin of the individual
Categories are foreign born and native born.
Nativity and migration are related.
17
Impact of Migration
Importation of “Third World” disease by immigrants from
developing countries

34. Leprosy during 1980s
Programmatic needs resulting from migration:
Specialized screening programs (tuberculosis and nutrition)
Familiarization with formerly uncommon (in U.S.) tropical
diseases
Healthy Migrant Effect
Observation that healthier, younger persons usually form the
majority of migrants
Often difficult to separate environmental influences in the host
country from selective factors operative among those who
choose to migrate
Religion
Certain religions prescribe lifestyles that may influence rates of
morbidity and mortality.
Example: Seventh Day Adventists
Follow vegetarian diet and abstain from alcohol and tobacco use
Have lower rates of CHD, reduced cancer risk, and lower blood
pressure
Similar findings for Mormons
18
Socioeconomic Status

35. Low social class is related to excess mortality, morbidity, and
disability rates.
Factors include:
Poor housing
Crowded conditions
Racial disadvantage
Low income
Poor education
Unemployment
19
Measurement of Social Class
Variables include:
Prestige of occupation or social position
Educational attainment
Income
Combined indices of two or more of the above variables
20
Hollingshead and Redlich

36. Studied association of socioeconomic status and mental illness
Classified New Haven, Connecticut, into five social classes
based on occupational prestige, education, and address
23
Hollingshead and Redlich Findings
Strong inverse association between social class and likelihood
of being a mental patient under treatment.
As social class increased, severity of mental illness decreased.
Type of treatment varied by social class.
24
Mental Health and Social Class
In the U.S., the highest incidence of severe mental illness
occurs among the lowest social classes.
Mental Health and Social Class: Two Hypotheses
Social causation explanation (breeder hypothesis) —conditions
associated with lower social class produce mental illness.

37. Downward drift hypothesis—Persons with severe mental
disorders move to impoverished areas.
21
Other Correlates of Low Social Class
Higher rate of infectious disease
Higher infant mortality rate and overall mortality rates
Lower life expectancy
Larger proportion of cancers with poor prognosis
May be due to delay in seeking health care
Low self-perceived health status
22
Characteristics of Place
Types of place comparisons:
International
Geographic (within-country) variations
Urban/rural differences
Localized occurrence of disease

38. 25
International Comparisons of Disease Frequency
World Health Organization (WHO) tracks international
variations in rates of disease.
Infectious and chronic diseases show great variation across
countries.
Variations are attributable to climate, cultural factors, dietary
habits, and health care access.
The U.S. fell in the bottom half of OECD countries for both
male and female life expectancy; Japan was highest.
26
Within-Country Variations in Rates of Disease
Due to variations in climate, geology, latitude, pollution, and
ethnic and racial concentrations
In U.S., comparisons can be made by region, state, and/or
county.
Examples include: higher rates of leukemia in Midwest; state by
state variations in infectious, vector-borne, parasitic diseases

39. 27
Urban/Rural Differences in Disease Rates
Urban
Diseases and mortality associated with crowding, pollution, and
poverty
Example: lead poisoning in inner cities
Homicide in central cities
Rural
Mortality (among all age groups) increases with decreasing
urbanization.
Health risk behaviors higher in rural South
28
Standard Metropolitan Statistical Areas (SMSAs)
Established by the U.S. Bureau of the Census to make regional
and urban/rural comparisons in disease rates

40. Metropolitan Statistical Areas (MSAs)
Provide a distinction between metropolitan and nonmetropolitan
areas by type of residence, industrial concentration, and
population concentration
35
Definition of MSA
Used to distinguish between metropolitan and nonmetropolitan
areas
Metropolitan area—large population nucleus together with
adjacent communities
Six urban-classification levels used by the National Center for
Health Statistics (refer to text.)
Census Tracts
Small geographic subdivisions of cities, counties, and adjacent
areas
Each tract contains about 4,000 residents.
Are designed to provide a degree of uniformity of population
economic status and living conditions in each tract
36

41. Localized Place Comparisons
Disease patterns are due to unique environmental or social
conditions found in particular area of interest. Examples
include:
Fluorosis: associated with naturally occurring fluoride deposits
in water.
Goiter: iodine deficiency formerly found in land-locked areas of
U.S.
29
Geographic Information Systems (GIS)
A method to provide a spatial perspective on the geographic
distribution of health conditions
A GIS produces a choroplath map that shows variations in
disease rates by different degrees of shading.
Reasons for Place Variation in Disease
Gene/environment interaction
Examples: sickle-cell gene; Tay-Sachs disease.
Influence of climate
Examples: yaws, Hansen’s disease

42. Environmental factors
Example: chemical agents linked to cancer
30
Characteristics of Time
Cyclic fluctuations
Point epidemics
Secular time trends
Clustering
Temporal
Spatial
31
Cyclic Fluctuations
Periodic changes in the frequency of diseases and health
conditions over time
Examples:
Birth rates
Higher heart disease mortality in winter
Influenza
Unintentional injuries
Meningococcal disease

43. Rotavirus infections
Cyclic Fluctuations (cont’d)
Related to changes in lifestyle of the host, seasonal climatic
changes, and virulence of the infectious agent
Common Source Epidemic
Outbreak due to exposure of a group of persons to a noxious
influence that is common to the individuals in the group
Types: point epidemic; continuous common source epidemic
Refer to Figure 4-22 for an example an influenza outbreak in a
residential facility.
Point Epidemics
The response of a group of people circumscribed in place and
time to a common source of infection, contamination, or other
etiologic factor to which they were exposed almost
simultaneously.
Examples: foodborne illness; responses to toxic substances;
infectious diseases.
32

44. Influenza-Related Illness at a Residential Facility
Secular Time Trends
Refer to gradual changes in the frequency of a disease over long
time periods.
Example is the decline of heart disease mortality in the U.S.
May reflect impact of public health programs, dietary
improvements, better treatment, or unknown factors.
33
Clustering
Case clustering--refers to an unusual aggregation of health
events grouped together in space and time
Temporal clustering: e.g., post-vaccination reactions,
postpartum depression
Spatial clustering: concentration of disease in a specific
geographic area, e.g., Hodgkin’s disease
35

45. Chapter 1
The History and Scope of Epidemiology
1
Learning Objectives
Define the term epidemiology
Define the components of epidemiology (determinants,
distribution, morbidity, and mortality)
Name and describe characteristics of the epidemiologic
approach
Discuss the importance of Hippocrates’ hypothesis and how it
differed from the common beliefs of the time
Discuss Graunt’s contributions to biostatistics and how they
affected modern epidemiology
Explain what is meant by the term natural experiments, and give
at least one example
2
2009 H1N1 Influenza
During April 2009, 2 cases of 2009 H1N1 came to the attention
of CDC.
The initial cases occurred in the U.S. and then expanded rapidly
worldwide.
By summer 2010, the epidemic subsided and an estimated 60

46. million cases had occurred in the U.S.
Heavily affected people were from 18 to 64 years old. See
Exhibit 1-1.
2006 Outbreak of Escherichia coli
Outbreak during late summer and fall of 2006
Affected 199 persons and caused 3 deaths
Caused 102 (51%) of ill persons to be hospitalized
A total of 31 patients (16%) were afflicted with hemolytic-
uremic syndrome (HUS).
Spread across 26 states
Fresh spinach linked to the outbreak
4
Epidemiology Defined
Epidemiology derives from "epidemic," a term which provides
an immediate clue to its subject matter. Epidemiology originates
from the Greek words, epi (upon) + demos (people) + logy
(study of).
6
3

47. Definition of Epidemiology
Epidemiology is concerned with the distribution and
determinants of health and diseases, morbidity, injuries,
disability, and mortality in populations.
Epidemiologic studies are applied to the control of health
problems in populations.
7
4
Key Aspects of This Definition
Determinants
Distribution
Population
Health phenomena
Morbidity and mortality
8
5
Determinants
Factors or events that are capable of bringing about a change in
health.

48. 9
6
Examples of Determinants
Biologic agents--bacteria
Chemical agents--carcinogens
Less specific factors--stress, drinking, sedentary lifestyle, or
high-fat diet
10
The Search for Determinants
Outbreak of Fear--Ebola virus in Kikwit, Zaire
Fear on Seventh Ave.--Legionnaires’ disease in New York City
Red Spots on Airline Flight Attendants--dye from life vests
Bioterrorism-Associated Anthrax Cases
11
7
Bioterrorism-Associated Anthrax Cases
Index case reported in Florida
Additional cases, including fatal cases, reported in New York,

49. New Jersey, Connecticut
Contaminated mail linked to some of the cases
12
Distribution
Frequency of disease occurrence may vary from one population
group to another.
13
8
Disease Distribution Examples
In 2006, death rates from CHD and stroke were higher among
African-Americans than among American Indians/Alaskan
natives, Asian/Pacific Islanders, or whites.
Coronary heart disease occurrence differs between Hispanics
and non-Hispanics.
14
Population
Epidemiology examines disease occurrence among population
groups, not individuals.
Epidemiology is often referred to as population medicine.
The epidemiologic description indicates variation by age
groups, time, geographic location, and other variables.

50. 15
9
Health Phenomena
Epidemiology investigates many different kinds of health
outcomes:
Infectious diseases
Chronic diseases
Disability, injury, limitation of activity
Mortality
Active life expectancy
Mental illness, suicide, drug addiction
16
10
Morbidity and Mortality
Morbidity--designates illness.
Mortality--refers to deaths that occur in a population or other
group.
Note that most measures of morbidity and mortality are defined
for specific types of morbidity or causes of death.
17

51. 11
Aims and Levels
To describe the health status of populations
To explain the etiology of disease
To predict the occurrence of disease
To control the occurrence of disease
18
12
Foundations of Epidemiology
Interdisciplinary
Methods and procedures—quantification
Use of special vocabulary
Epidemic frequency of disease
19
13
Epidemiology Is Interdisciplinary
Epidemiology is an interdisciplinary field that draws from

52. biostatistics and the social and behavioral sciences, as well as
from the medically related fields of toxicology, pathology,
virology, genetics, microbiology, and clinical medicine.
20
14
Quantification
Quantification is a central activity of epidemiology.
Epidemiologic measures often require counting the number of
cases of disease.
Disease distributions are examined according to demographic
variables such as age, sex, race, and other variables, such as
exposure category and clinical features.
22
15
Epidemic
“The occurrence in a community or region of cases of an illness
(or an outbreak) clearly in excess of expectancy…”

53. Relative to usual frequency of the disease
23
17
Key Terms in “Epidemic”
Communicable disease
An illness caused by an infectious agent that can be transmitted
from one person to another.
Infectious disease
A synonym for a communicable disease
Outbreak
A localized disease epidemic, e.g., in a town or health care
facility
Concept of Epidemic and Non-Infectious Diseases
Some examples that use the concept of an epidemic are:
Love Canal
Red spots among airline flight attendants
Toxic Shock Syndrome
Brown lung disease
Asbestosis among shipyard workers
Diseases associated with lifestyle
25
Pandemic
“ . . . an epidemic on a worldwide scale; during a pandemic,

54. large numbers of persons may be affected and a disease may
cross international borders.” An example is a flu pandemic.
26
Ascertainment of Epidemics
Surveillance
Epidemic threshold
27
19
Surveillance
The systematic collection of data pertaining to the occurrence of
specific diseases.
Analysis and interpretation of these data.
Dissemination of disease-related information
Common activities include monitoring food born disease
outbreaks and tracking influenza.
28
Epidemic Threshold
The minimum number of cases (or deaths) that would support
the conclusion than an epidemic was underway.
This is based on statistical projections.
Figure1-6 demonstrates that the combined pneumonia and

55. influenza deaths peaked substantially above the epidemic
threshold during early 2008, late 2009, and early 2011.
29
Historical Antecedents
The Cholera Fountain
Environment and disease
The Black Death
Use of mortality counts
Smallpox vaccination
Use of natural experiments
William Farr
Identification of specific agents of disease
The 1918 influenza pandemic
31
20
The Cholera Fountain
Dresden, Germany
Dresden, Germany, was spared from a deadly cholera epidemic
during 19th Century.
Mid 1800s--Residents constructed a Cholera Fountain to express

56. their gratitude for escaping the cholera epidemic that threatened
the city.
The Environment
Hippocrates wrote On Airs, Waters, and Places in 400 BC.
He suggested that disease might be associated with the physical
environment.
This represented a movement away from supernatural
explanations of disease causation.
34
22
The Black Death
Occurred between 1346-1352.
Claimed one-quarter to one-third of population of Europe.
Use of Mortality Counts
John Graunt, in 1662, published Natural and Political
Observations Made upon the Bills of Mortality.

57. 36
23
John Graunt’s Contributions
Recorded seasonal variations in births and deaths
Showed excess male over female differences in mortality
Known as the “Columbus” of biostatistics
See Yearly Mortality Bill for 1632: The 10 Leading Causes of
Mortality in Graunt’s Time.
37
Edward Jenner
Jenner conducted an experiment to create a smallpox vaccine.
He developed a method for smallpox vaccination.
In 1978 smallpox was finally eliminated worldwide.
Since 1972, routine vaccination of the nonmilitary population of
the U.S. has been discontinued.
Use of Natural Experiments
John Snow was an English physician and anesthesiologist.
He investigated a cholera outbreak that occurred during the
mid-19th century in Broad Street, Golden Square, London.
39
24

58. Snow’s Contributions
Linked the cholera epidemic to contaminated water supplies
Used a spot map of cases and tabulation of fatal attacks and
deaths
40
Snow’s Natural Experiment
Two different water companies supplied water from the Thames
River to houses in the same area.
The Lambeth Company moved its source of water to a less
polluted portion of the river.
Snow noted that during the next cholera outbreak those served
by the Lambeth Company had fewer cases of cholera.
41
Natural Experiment
Refers to “naturally occurring circumstances in which subsets
of the population have different levels of exposure to a
supposed causal factor in a situation resembling an actual
experiment, where human subjects would be randomly allocated
to groups. The presence of persons in a particular group is
typically nonrandom.”
42

59. Ignaz Semmelweis
Mid-19th century, Viennese hospital
Clinical assistant in obstetrics and gynecology
Observed higher mortality rate among the women on the
teaching wards for medical students and physicians than on the
teaching wards for midwives
Postulated that medical students and physicians had
contaminated their hands during autopsies
Introduced the practice of hand washing
William Farr
Appointed compiler of abstracts in England, 1839
Provided foundation for classification of diseases (ICD system)
Used data such as census reports to study occupational mortality
in England
Examined linkage between mortality rates and population
density
44
Koch's Postulates
Microorganism must be observed in every case of the disease
Microorganism must be isolated and grown in pure culture
Pure culture must, when inoculated into a susceptible animal,
reproduce the disease
Microorganism must be observed in, and recovered from,
diseased animal
45
28

60. The 1918 Influenza Pandemic
“The Mother of All Pandemics” and Spanish Flu
Occurred between 1918 and 1919
Killed 50- to 100 million persons worldwide
2.5% case-fatality rate versus 0.1% for other influenza
pandemics
Deaths most frequent among 20- to 40-year-olds
Other Historical Developments
Alexander Fleming discovered the antimicrobial properties of
the mold: led to the discovery of the antibiotic penicillin.
Alexander Langmuir established CDC’s Epidemic Intelligence
Service.
Wade Hampton Frost was the first professor of epidemiology in
the U.S.
Joseph Goldberger discovered the cure for pellagra.
47
Recent Applications of Epidemiology
The Framingham Heart Study (ongoing since 1948) investigates
coronary heart disease risk factors.
Smoking and lung cancer; e.g., Doll and Peto’s study of British
doctors’ smoking
AIDS, chemical spills, breast cancer screening, second-hand
cigarette smoke
Association between HPV and cervical cancer
48

61. 30
Additional Applications of Epidemiology
Infectious diseases
SARS, pandemic influenza 2009 H1N1, Avian influenza
Environmental health
Chronic diseases
Lifestyle and health promotion
Psychological and social epidemiology
Molecular and genetic epidemiology
49
31
Study Designs: Cohort Studies
Chapter 7
Learning Objectives
Differentiate cohort studies from other study designs
List main characteristics, advantages, and disadvantages of
cohort studies
Describe three research questions that lend themselves to cohort
studies
Calculate and interpret a relative risk

62. Give three examples of published studies discussed in this
chapter
Temporality
Temporality refers to the timing of information about cause and
effect.
Did the information about cause and effect refer to the same
point in time?
Or, was the information about the cause garnered before or after
the information about the effect?
Limitations of Other Study Designs
Demonstrating temporality is a difficulty of most observational
studies.
Limitations of Other Study Designs (cont’d)
Cross-sectional and case-control study designs are based on
exposure and disease information that is collected at the same
time.
Advantage: Efficient for generating and testing hypotheses.
Disadvantage: Leads to challenges regarding interpretation of
results.
Limitations of Other Study Designs (cont’d)
Cross-sectional studies:
Present difficulties in distinguishing the exposures from the
outcomes of the disease, especially if the outcome marker is a
biological or physiological parameter.
Limitations of Other Study Designs (cont’d)
Case-control studies:

63. Raise concerns that recall of past exposures differs between
cases and controls.
Limitations of Other Study Designs (cont’d)
There has been no actual lapse of time between measurement of
exposure and disease.
None of the previous study designs is well suited for uncommon
exposures.
What is a cohort?
A cohort is defined as a population group, or subset thereof,
that is followed over a period of time.
The term cohort is said to originate from the Latin cohors,
which referred to one of ten divisions of an ancient Roman
legion.
What is a cohort? (cont’d)
Cohort group members experience a common exposure
associated with a specific setting (e.g., an occupational cohort
or a school cohort) or they share a non-specific exposure
associated with a general classification (e.g., a birth cohort—
being born in the same year or era).
Cohort Effect
The influence of membership in a particular cohort.
Example: Tobacco use in the U.S.
Fewer than 5% of population smoked around the early 1900s.
Free cigarettes for WWI troops increased prevalence of smoking
in the population.
During WWI, age of onset varied greatly; then people began
smoking earlier in life.
One net effect was a shift in the distribution of the age of onset

64. of lung cancer.
Cohort Analysis
The tabulation and analysis of morbidity or mortality rates in
relationship to the ages of a specific group of people (cohort)
identified at a particular period of time and followed as they
pass through different ages during part or all of their life span.
Wade Hampton Frost
Popularized cohort analysis method.
Arranged tuberculosis mortality rates in a table with age on one
axis and year of death on the other.
One can quickly see the age-specific mortality for each of the
available years on one axis and the time trend for each age
group on the other.
Wade Hampton Frost
Life Table Methods
Give estimates for survival during time intervals and present the
cumulative survival probability at the end of the interval.
Example: Life tables can be constructed to portray the survival
times of patients in clinical trials.
Life Table Methods (cont’d)
There are two life table methods:
Cohort Life Table
Period (Current) Life Table

65. Life Table Methods (cont’d)
Cohort life table:
Shows the mortality experience of all persons born during a
particular year, such as 1900.
Period life table:
Enables us to project the future life expectancy of persons born
during the year as well as the remaining life expectancy of
persons who have attained a certain age.
Describing the Mortality Experience of the Population
Years of Potential Life Lost (YPLL)
Disability-adjusted life years (DALYs)
YPLL
Years of potential life lost (YPLL)
Computed for each individual in a population by subtracting
that person’s life span from the average life expectancy of the
population
DALYs
Disability-adjusted life years (DALYs)
Adds the time a person has a disability to the time lost to early
death
Survival Curves
A method for portraying survival times
In order to construct a survival curve, the following information
is required:
Time of entry into the study
Time of death or other outcome

66. Status of patient at time of outcome, e.g., dead or censored
(patient is lost to follow-up)
Cohort Studies
Start with a group of subjects who lack a positive history of the
outcome of interest and are at risk for the outcome
Include at least two observation points: one to determine
exposure status and eligibility and a second (or more) to
determine the number of incident cases
Cohort Studies (cont’d)
Permit the calculation of incidence rates
Can be thought of as going from cause to effect
The individual forms the unit of observation and the unit of
analysis.
Involve the collection of primary data, although secondary data
sources are used sometimes for both exposure and disease
assessment
Cohort Studies
Timing of Data Collection
Sampling and Cohort Formation Options
Cohort studies differ according to sampling strategy used.
The two strategies are population-based samples and exposure-
based samples.

67. Population-Based Cohort Studies
The cohort includes either an entire population or a
representative sample of the population.
Population-based cohorts have been used in studies of coronary
heart disease.
Framingham Study
Conducted in Framingham, Massachusetts
Ongoing study of CHD initiated in 1948
Used a random sample of 6,500 from targeted age range of 30 to
59 years
Tecumseh Study
Conducted in Tecumseh, Michigan
A total community cohort study
Examined the contribution of environmental and constitutional
factors to the maintenance of health and origins of illness
Started in 1959-1960 and enrolled 8,641 (88% of the
community)
Population-Based Cohort Studies (cont’d)
Exposures unknown until the first period of observation when
exposure information is collected
Examples: After administration of questionnaires, collection of
biologic samples, and clinical examinations, there can be two or
more levels of exposure.
Exposure-Based Cohort Studies
These studies overcome limitations of population-based cohort
studies, which are not efficient for rare exposures.

68. Certain groups, such as occupational groups, may have higher
exposures than the general population to specific hazards.
Definition of Exposure-Based Cohort
An exposure-based cohort is made up of subjects with a
common exposure.
Examples:
Workers exposed to lead during battery production
Childhood cancer survivors
Veterans
College Graduates
Comparison (Non-Exposed Group)
Cohort studies involve the comparison of disease rates between
exposed and non-exposed groups.
The comparison group is similar in demographics and
geography to the exposed group, but lacks the exposure.
In an occupational setting, several categories of exposure may
exist.
Outcome Measures
Discrete Events
Single events and multiple occurrences
Levels of Disease Markers
Changes in Disease Markers
Rate of change, change in level within time
Temporal Differences in Cohort Designs
There are several variations in cohort designs that depend on
the timing of data collection.
These variations are:
prospective cohort studies

69. retrospective cohort studies
Prospective Cohort Study
Purely prospective in nature; characterized by determination of
exposure levels at baseline (the present), and follow -up for
occurrence of disease at some time in the future
Advantages of Prospective Cohort Studies
Enable the investigator to collect data on exposures; the most
direct and specific test of the study hypothesis
The size of the cohort is under greater control by the
investigators
Advantages of Prospective Cohort Studies (cont’d)
Biological and physiological assays can be performed with
decreased concern that the outcome will be affected by the
underlying disease process.
Direct measures of the environment (e.g., indoor radon levels,
electromagnetic field radiation, cigarette smoke concentration)
can be made.
Retrospective Cohort Study
Despite substantial benefits of prospective cohort studies,
investigators have to wait for cases to accrue.
Retrospective cohort studies make use of historical data to
determine exposure level at some baseline in the past.
Advantages of Retrospective Cohort Studies
A significant amount of follow-up may be accrued in a
relatively short period of time.
The amount of exposure data collected can be quite extensive

70. and available to the investigator at minimal cost.
Historical Prospective Cohort Study
A design that makes use of both retrospective features (to
determine baseline exposure) and prospective features (to
determine disease incidence in the future)
Also known as an ambispective cohort study
Practical Considerations Regarding Cohort Studies
Availability of exposure data
Size and cost of the cohort used
Data collection and data management
Follow-up issues
Sufficiency of scientific justification
Availability of Exposure Data
High quality historical exposure data are absolutely essential
for retrospective cohort studies.
Need to trade off between a retrospective study design (with the
benefits of more immediate follow-up time) and collection of
primary exposure data in a prospective cohort design.
Size and Cost of the Cohort
The larger the size of the cohort, the greater the opportunity to
obtain findings in a timely manner.
Resource constraints typically influence design decisions.
Data Collection and Data Management
Larger studies are more demanding than smaller ones;

71. challenges due to data collection and data management.
Explicit protocols for quality control (e.g., double entry of data
and scannable forms) should be considered in the design and
implementation stage.
Data Collection and Data Management (cont’d)
Organizational and administrative burdens are increased when
there are multiple levels of data collection (such as phone
interviews, mailed questionnaires, consent forms to access
medical records).
Follow-up Issues
There are two types of follow-up:
Active follow-up
Passive follow-up
Active Follow-up
The investigator, through direct contact with the cohort, must
obtain data on subsequent incidence of the outcome (disease,
change in risk factor, change in biological marker).
Accomplished through follow-up mailings, phone calls, or
written invitations to return to study sites/centers.
Active Follow-up (cont’d)
Example: Minnesota Breast Cancer Family Study
Mailed survey
A reminder postcard 30 days later
A second survey
A telephone call to non-responders
Passive Follow-up

72. Does not require direct contact with cohort members.
Possible when databases containing the outcomes of interest are
collected and maintained by organizations outside the
investigative team.
Example: Used in the Iowa Women’s Health Study.
Sufficiency of Scientific Justification
There should be considerable scientific rationale for a cohort
study.
Additional justification for cohort studies may come from
laboratory experiments or animal studies.
Cohort studies are the only observational study design that
permits examination of multiple outcomes.
Cohort Studies:
Measures of Effect
Relative risk is the ratio of the risk of disease or death among
the exposed to the risk among the unexposed.
Recall that risk is estimated in epidemiologic studies only by
the cumulative incidence.
When the relative risk is calculated with incidence rates or
incidence density, then the term rate ratio is more precise.
Relative Risk
Relative risk =
Incidence rate in the exposed
Incidence rate in the non-exposed
Relative Risk
Using the notation from the 2 by 2 table, the relative risk can be
expressed as
[A/(A+B)] / [C/(C+D)]

73. Measures of Association (cont’d)
Disease Status
Incidence
Exposure Yes No Totals Total
Status
Yes A B A+B A/(A+B)
No C D C+D C/(C+D)
A + C B + D N
Relative Risk [A/A+B]/[C/C+D]
Cohort Studies:
Sample Calculation
Is there an association between child abuse and suicide attempts
among chemically dependent adolescents?
Source: Deykin EY, Buka SL. Am J Public Health.
1994;84:634-639.
Sample Calculation (cont’d)
Examples of Major Cohort Studies
The Alameda County Study
Studied factors associated with health and mortality
Involved residents of Alameda County, CA, ages 16-94 years
Data collected through mailed questionnaires; telephone
interviews or home interviews of non-respondents
Follow-up with same procedures at years 9, 18, and 29

74. Examples of Major Cohort Studies (cont’d)
Honolulu Heart Program
Studied coronary heart disease and stroke in men of Japanese
ancestry
Involved men of Japanese ancestry living on Oahu, HI, ages 45-
65 years
Data were collected through mailed questionnaires, interviews,
and clinic examinations.
Examples of Major Cohort Studies (cont’d)
Nurses’ Health Study
Originally studied oral contraceptive use; expanded to women’s
health
Married female R.N.s ages 30-55 years
Data collected through mailed questionnaires
Follow-up every 2 years; toenail sample at year 6 and blood
sample at year 13
Nested Case-Control Studies
A nested case-control study is defined as a type of case-control
study “. . . in which cases and controls are drawn from the
population in a cohort study.”
Example: nested case-control breast cancer study
Controls are a subset of the source population for the cohort
study of breast cancer.
Cases of breast cancer identified from the cohort study would
comprise the cases.
Advantages of Nested Case- Control Studies
Provide a degree of control over confounding factors.
Reduce cost because exposure information is collected from a
subset of the cohort only.

75. An example is an investigation of suicide among electric utility
workers.
Strengths of Cohort Studies
Permit direct determination of risk.
Time sequencing of exposure and outcome.
Can study multiple outcomes.
Can study rare exposures.
Limitations of Cohort Studies
Take a long time
Costly
Subjects lost to follow-up
Table 7-6
Table 7-6 summarizes various study designs by comparing their
characteristics, advantages, and disadvantages.
Chapter 10
Data Interpretation Issues
Learning Objectives
Distinguish between random and systematic errors
State and describe sources of bias

76. Identify techniques to reduce bias at the design and analysis
phases of a study
Define what is meant by the term confounding and provide three
examples
Describe methods to control confounding
Validity of Study Designs
The degree to which the inference drawn from a study, is
warranted when account it taken of the study, methods, the
representativeness of the study sample, and the nature of the
population from which it is drawn.
2
Validity of Study Designs
Two components of validity:
Internal validity
External validity
2

77. Internal Validity
A study is said to have internal validity when there have been
proper selection of study groups and a lack of error in
measurement.
Concerned with the appropriate measurement of exposure,
outcome, and association between exposure and disease.
3
External Validity
External validity implies the ability to generalize beyond a set
of observations to some universal statement.
A study is externally valid, or generalizable, if it allows
unbiased inferences regarding some other target population
beyond the subjects in the study.
4
Sources of Error in Epidemiologic Research
Random errors

78. Systematic errors (bias)
5
Random Errors
Reflect fluctuations around a true value of a parameter because
of sampling variability.
6
Factors That Contribute to Random Error
Poor precision
Sampling error
Variability in measurement
7

79. Poor Precision
Occurs when the factor being measured is not measured sharply.
Analogous to aiming a rifle at a target that is not in focus.
Precision can be increased by increasing sample size or the
number of measurements.
Example: Bogalusa Heart Study
8
Sampling Error
Arises when obtained sample values (statistics) differ from the
values (parameters) of the parent population.
Although there is no way to prevent a non-representative sample
from occurring, increasing the sample size can reduce the
likelihood of its happening.
9
Variability in Measurement
The lack of agreement in results from time to time reflects

80. random error inherent in the type of measurement procedure
employed.
10
Bias (Systematic Errors)
“Deviation of results or inferences from the truth, or processes
leading to such deviation. Any trend in the collection, analysis,
interpretation, publication, or review of data that can lead to
conclusions that are systematically different from the truth.”
11
Factors That Contribute to Systematic Errors
Selection bias
Information bias
Confounding
12

81. Selection Bias
Refers to distortions that result from procedures used to select
subjects and from factors that influence participation in the
study.
Arises when the relation between exposure and disease is
different for those who participate and those who theoretically
would be eligible for study but do not participate.
Example: Respondents to the Iowa Women’s Health Study were
younger, weighed less, and were more likely to live in rural,
less affluent counties than nonrespondents.
13
Information Bias
Can be introduced as a result of measurement error in
assessment of both exposure and disease.
Types of information bias:
Recall bias: better recall among cases than among controls.
Example: Family recall bias
14

82. Information Bias (cont’d)
Interviewer/abstractor bias--occurs when interviewers probe
more thoroughly for an exposure in a case than in a control.
Prevarication (lying) bias--occurs when participants have
ulterior motives for answering a question and thus may
underestimate or exaggerate an exposure.
15
Confounding
The distortion of the estimate of the effect of an exposure of
interest because it is mixed with the effect of an extraneous
factor.
Occurs when the crude and adjusted measures of effect are not
equal (difference of at least 10%).
Can be controlled for in the data analysis.
16

83. Criteria of Confounders
To be a confounder, an extraneous factor must satisfy the
following criteria:
Be a risk factor for the disease.
Be associated with the exposure.
Not be an intermediate step in the causal path between exposure
and disease.
17
Simpson’s Paradox as an Example of Confounding
Simpson’s paradox means that an association in observed
subgroups of a population may be reversed in the entire
population.
Illustrated by examining the data (% of black and gray hats)
first according to two individual tables and then by combining
all the hats on a single table.
Simpson’s Paradox (cont’d)
When the hats are on separate tables, a greater proportion of
black hats than gray hats on each table fit.
On table 1:
90% of black hats fit
85% of gray hats fit
On table 2:
15% of black hats fit
10% of gray hats fit

84. Simpson’s Paradox (cont’d)
19
Simpson’s Paradox (cont’d)
When the man returns the next day and all of the hats are on one
table:
60% of gray hats fit (18 of 30)
40% of black hats fit (12 of 30)
Note that combining all of the hats on one table is analogous to
confounding.
Examples of Confounding
Air pollution and bronchitis are positively associated. Both are
influenced by crowding, a confounding variable.
The association between high altitude and lower heart disease
mortality also may be linked to the ethnic composition of the
people in these regions.

85. 18
Techniques to Reduce Selection Bias
Develop an explicit (objective) case definition.
Enroll all cases in a defined time and region.
Strive for high participation rates.
Take precautions to ensure representativeness.
20
Reducing Selection Bias Among Cases
Ensure that all medical facilities are thoroughly canvassed.
Develop an effective system for case ascertainment.
Consider whether all cases require medical attention; consider
possible strategies to identify where else the cases might be
ascertained.
Reducing Selection Bias Among Controls
Compare the prevalence of the exposure with other sources to
evaluate credibility.

86. Attempt to draw controls from a variety of sources.
21
Techniques to Reduce Information Bias
Use memory aids; validate exposures.
Blind interviewers as to subjects’ study status.
Provide standardized training sessions and protocols.
Use standardized data collection forms.
Blind participants as to study goals and classification status.
Try to ensure that questions are clearly understood through
careful wording and pretesting.
22
Methods to Control Confounding
Prevention strategies--attempt to control confounding through
the study design itself.
Three types of prevention strategies:
Randomization
Restriction
Matching

87. Two types of analysis strategies:
Stratification
Multivariate techniques
23
Randomization
Attempts to ensure equal distributions of the confounding
variable in each exposure category.
Advantages:
Convenient, inexpensive; permits straightforward data analysis.
Disadvantages:
Need control over the exposure and the ability to assign
subjects to study groups.
Need large sample sizes.
24
Restriction
May prohibit variation of the confounder in the study groups.
For example, restricting participants to a narrow age category
can eliminate age as a confounder.

88. Provides complete control of known confounders.
Unlike randomization, cannot control for unknown confounders.
25
Matching
Matches subjects in the study groups according to the value of
the suspected or known confounding variable to ensure equal
distributions.
Frequency matching--the number of cases with particular match
characteristics is tabulated.
Individual matching--the pairing of one or more controls to each
case based on similarity in sex, race, or other variables.
26
Matching (cont’d)
Advantages:
Fewer subjects are required than in unmatched studies of the
same hypothesis.
May enhance the validity of a follow-up study.
Disadvantages:

89. Costly because extensive searching and recordkeeping are
required to find matches.
27
Two Analysis Strategies to Control Confounding
Stratification--analyses performed to evaluate the effect of an
exposure within strata (levels) of the confounder.
Multivariate techniques--use computers to construct
mathematical models that describe simultaneously the influence
of exposure and other factors that may be confounding the
effect.
28
Advantages of Stratification
Performing analyses within strata is a direct and logical
strategy.
Minimum assumptions must be satisfied for the analysis to be
appropriate.
The computational procedure is straightforward.

90. 29
Disadvantages of Stratification
Small numbers of observations in some strata.
A variety of ways to form strata with continuous variables.
Difficulty in interpretation when several confounding factors
must be evaluated.
Categorization results in loss of information.
30
Multivariate Techniques
Advantages:
Continuous variables do not need to be converted to categorical
variables.
Allow for simultaneous control of several exposure variables in
a single analysis.
Disadvantages:
Potential for misuse.

91. 31
Publication Bias
Occurs because of the influence of study results on the chance
of publication.
Studies with positive results are more likely to be published
than studies with negative results.
Publication Bias (cont’d)
May result in a preponderance of false-positive results in the
literature.
Bias is compounded when published studies are subjected to
meta-analysis.
Chapter 6
Study Designs: Ecologic, Cross-Sectional, Case-Control
Learning Objectives
Define the basic differences between observational and
experimental epidemiology
Identify an epidemiologic study design by its description
List the main characteristics, advantages, and disadvantages of
ecologic, cross-sectional, and case-control studies
Describe sample designs used in epidemiologic research
Calculate and interpret an odds ratio

92. How Study Designs Differ
Number of observations made
Directionality of exposure
Data collection methods
Timing of data collection
Unit of observation
Availability of subjects
Observational vs. Experimental Approaches
Manipulation of study factor
Was exposure of interest controlled by investigator?
Randomization of study subjects
Was there use of a random process to determine exposure of
study subjects?
Typology of Epidemiologic Research
Overview of Study Designs
Experimental studies
Quasi-experimental studies
Observational studies
Descriptive studies: cross-sectional surveys
Analytic studies: many ecologic studies, case-control studies,
cohort studies
The 2 by 2 Table Represents the Association Between Exposure
and Disease Status

93. Ecologic Studies
The unit of analysis is the group, not the individual.
They can be used for generating hypotheses .
The level of exposure for each individual in the unit being
studied is unknown.
Generally makes use of secondary data.
Advantageous with cost and duration.
Types of Ecologic Studies
Ecologic comparison study—involves an assessment of the
correlation between exposure rates and disease rates among
different groups over the same time period.
Ecologic trend study—involves correlation of changes in
exposure with changes in disease within the same community,
country, or other aggregate unit.
Example of an Ecologic Correlation
The association between breast cancer and dietary fat for 39
countries.
High intakes of dietary fats associated with high rates of breast
cancer mortality.
Examples of Questions Investigated by Ecologic Studies
Is the ranking of cities by air pollution levels associated with
the ranking of cities by mortality from cardiovascular disease,

94. adjusting for differences in average age, percent of the
population below poverty level, and occupational structure?
What are long-term trends (1950-1995) for mortality from the
major cancers in the US, Canada, and Mexico?
Applications of Ecologic Approach
The effect of fluoridation of the water supply on hip fractures
The association of naturally occurring fluoride levels and
cancer incidence rates
The relationship between neighborhood or local area social
characteristics and health outcomes
The Ecologic Fallacy: Definition
Observations made at the group level may not represent the
exposure-disease relationship at the individual level.
The ecologic fallacy occurs when incorrect inferences about the
individual are made from group level data.
Implications of the Ecologic Fallacy
The conclusions obtained from an ecologic study may be the
reverse of those from a study that collects data on individual
subjects.
The Ecologic Study: Example
An ecologic study examines 10 individuals who go into the sun.
The study finds that 7 persons (70%) have sunburned foreheads
although 6 persons (60%) wore hats.
The expected number of sunburned foreheads is 4 (the number
who did not wear hats).
The media report that wearing hats will not protect you from
sunburn.

95. What the Individual Data Show
Individual Data (cont’d)
From the individual data, one observes that 100% of persons (4)
who did not wear hats were sunburned.
Among persons who wore hats (6), only 50% were sunburned.
This conclusion reverses the conclusion from the ecologic data,
i.e., that wearing hats affords little protection from sunburn.
Ecologic Studies: Advantages and Disadvantages
Advantages
Quick, simple, inexpensive
Good approach for generating hypotheses when a disease is of
unknown etiology
Disadvantages
Ecological fallacy
Imprecise measurement of exposure and disease
Cross-Sectional Study
Also termed prevalence study
Exposure and disease measures obtained at the individual level.
Single period of observation
Exposure and disease histories are collected simultaneously.
Both probability and non-probability sampling is used.
Cross-Sectional Study: Examples
Surveys of smokeless tobacco use among high school students
Prevalence surveys of the number of vasectomies performed
Prevalence surveys of cigarette smoking among Cambodian

96. Americans in Long Beach, California
Uses of Cross-Sectional Studies
Hypothesis generation
Intervention planning
Planning health services and administering medical care
facilities
Estimation of the magnitude and distribution of a health
problem
Examine trends in disease or risk factors that can vary over time
Limitations of Cross-Sectional Studies
Limited usefulness for inferring disease etiology
Do not provide incidence data
Cannot study low prevalence diseases
Cannot determine temporality of exposure and disease
Overview of Case-Control Studies
In a case-control study with two groups, one group has the
disease of interest (cases) and a comparable group is free from
the disease (controls).
The case-control study identifies possible causes of disease by
finding out how the two groups differ with respect to exposure
to some factor.
Characteristics of the Case-Control Study
A single point of observation
Unit of observation and the unit of analysis are the individual
Exposure is determined retrospectively
Does not directly provide incidence data
Data collection typically involves a combination of both
primary and secondary sources.

97. Selection of Cases
Two tasks are involved in case selection:
Defining a case conceptually
Identifying a case operationally
Sources of Cases
Need to define a case conceptually
Ideally, identify and enroll all incident cases in a defined
population in a specified time period
A tumor registry or vital statistics bureau may provide a
complete listing of all cases
Medical facilities also may be a source of cases, but not always
incident cases
Selection of Controls
The ideal controls should have the same characteristics as the
cases (except for the exposure of interest).
If the controls were equal to the cases in all respects other than
disease and the hypothesized risk factor, one would be in a
stronger position to ascribe differences in disease status to the
exposure of interest.
Sources of Controls
Population-based controls--Obtain a list that contains names and
addresses of most residents in the same geographic area as the
cases.
A driver’s license list would include most people between the
ages of 16 and 65.
Tax lists, voting lists, and telephone directories
Patients from the same hospital as the cases
Relatives of cases

98. Measures of Association Used in Case-Control Studies
Case-Control Studies
Sample Calculation
On the association between chili pepper consumption and
gastric cancer risk: a population-based case-control study
conducted in Mexico City
Source: Lopez-Carillo, et al. Am J Epidemiol. 1994;139:263-
71.
Sample Calculation (cont’d)Chili Pepper ConsumptionCases of
Gastric CancerControlsYesA = 204B = 552NoC = 9D = 145
The OR (unadjusted for age and sex) is:
AD = (204)(145) = 5.95
BC (552)(9)
Interpretation of an Odds Ratio (OR)
OR = 1 implies no association.
Assuming statistical significance:
OR = 2 suggests cases were twice as likely as controls to be
exposed.
OR<1 suggests a protective factor.
Odds Ratio (cont’d)
An OR provides a good approximation of risk when:
Controls are representative of a target population.
Cases are representative of all cases.

99. The frequency of disease in the population is small.
Examples of Case-Control Studies
Young women’s cancers resulting from utero exposure to
diethylstilbestrol
Green tea consumption and lung cancer
Maternal anesthesia and development of fetal birth defects
Passive smoking at home and risk of acute myocardial
infarction
Household antibiotic use and antibiotic resistant pneumococcal
infection
Advantages of Case-Control Studies
Tend to use smaller sample sizes than surveys or prospective
studies
Quick and easy to complete
Cost effective
Useful for studies of rare diseases
Limitations of Case-Control Studies
Unclear temporal relationships between exposures and diseases
Use of indirect estimate of risk
Representativeness of cases and controls often unknown
Key Points to Remember
Descriptive studies: cross-sectional surveys (hypothesis
generation)
Analytic studies: ecologic, case-control, and cohort (hypothesis
testing)
Conclusion

100. Study designs differ in a number of key respects, including the
unit of observation; the unit of analysis; the timing of exposure
data in relation to occurrence of disease endpoint; complexity;
rigor; and amount of resources required.
Disease Status
Yes (Cases)
No (Controls)
Yes
A
B
Exposure
Status
No
C
D
A+C B+D
Odds A/C B/D
Odds Ratio AD/BC
Disease Status

101. Yes (Cases)
No (Controls)Exposure
Status
Yes
A
B
No
C
D
A+C
B+D
Odds
A/C
B/D
Odds Ratio
AD/BC
Chapter 2
Practical Applications of Epidemiology
1
Learning Objectives
Discuss uses and applications of epidemiology
Define the influence of population dynamics on community

102. health
State how epidemiology may be used for operations research
Discuss the clinical applications of epidemiology
Cite causal mechanisms from the epidemiologic perspective
2
Seven Uses for Epidemiology
Health Status and Health Services
Study history of the health of populations
Diagnose the health of the community
Examine the working of health services
Disease Etiology
Estimate the individual risks and chances
Identify syndromes
Complete the clinical picture
Search for causes
Health Status and Health Services
Describing the occurrence of disease in the community
Planning for allocation of resources
Public health practitioners
Administrators
Evaluating programs, e.g., public health service programs
4
Disease Etiology
Epidemiologists continue to search for clues as to the nature of
disease.

103. Knowledge that is acquired may be helpful in efforts to prevent
the occurrence.
5
Historical Use of Epidemiology
Refers to the study of past and future trends in health and
illness
For example: Secular trends--changes in disease frequency over
time
6
Examples of Trends
Chronic diseases have replaced acute infectious diseases as the
major causes of morbidity and mortality.
In 2009, the leading causes of U.S. deaths were heart disease,
cancer, and chronic lower respiratory disease.
Increases were reported for Alzheimer’s disease, kidney
disease, and hypertension.
7
Factors Affecting Reliability of Observed Changes
Lack of comparability over time due to altered diagnostic

104. criteria
Aging of the general population
Changes in the fatal course of the condition
9
Four Trends in Disorders
Disappearing
Residual
Persisting
New epidemic
10
Disappearing Disorders
This category refers to conditions that were once common but
are no longer present in epidemic form.
Examples include smallpox, poliomyelitis, and measles.
Brought under control by immunizations, improvement in
sanitary conditions, and the use of antibiotics and other
medications led to eradication of these diseases
11
Residual Disorders
Conditions for which the key contributing factors are largely
known
Methods of control not implemented effectively
Examples:
STDs

105. Perinatal and infant mortality among low SES persons
Problems associated with alcohol and tobacco use
12
Persisting Disorders
Diseases for which there is no effective method of prevention or
no known cure
Examples: certain types of cancer and mental disorders
13
New Epidemic Disorders
Diseases that are increasing in frequency
Examples: Lung cancer, AIDS, Obesity, Type 2 diabetes
The emergence of new epidemics of diseases may be a result of
increased life expectancy of the population, new environmental
exposures, or changes in lifestyle, diet, and other practices.
14
Predictions About the Future
A population pyramid represents the age and sex composition of
the population of an area or country at a point in time.
By examining the distribution of a population by age and sex,
one may view the impact of mortality from acute and chronic
conditions.

106. 15
Trends in the Age and Sex Distributions
Developing countries
In 1950 and 1990, countries had a triangular population
distribution, which is associated with high death rates from
infections, high birth rates, and other conditions.
By 2030, improvements in health are likely to result in greater
survival of younger persons, causing a projected change in the
shape of the population distribution.
16
Trends in the Age and Sex Distributions
Developed countries
Manifest a rectangular population distribution
Infections take a smaller toll and cause a greater proportion of
children to survive into old age
Residents enjoy greater life expectancy
The population of developed countries will grow increasingly
older due to continuing advances in medical care
17
Population Dynamics
Denotes changes in the demographic structure of populations
associated with such factors as births and deaths and
immigration and emigration
Two types of populations
Fixed populations
Dynamic populations

107. 18
Population Terms
Fixed population
Adds no new members and, as a result, decreases in size due to
deaths only
Examples: survivors of the 9-11 terrorist attack in New York,
residents of New Orleans during Hurricane Katrina, and persons
who had a medical procedure such as hip replacement
19
Population Terms
Dynamic population
Adds new members through immigration and births or loses
members through emigration and deaths
Example: the population of a country, city, or state in the
United States
20
Influences on Population Size
Three major factors affect the sizes of population births, deaths,
and migration.
Population in equilibrium or a steady state
The three factors do not contribute to net increases or decreases
in the number of persons.

108. Influences on Population Size
Population increasing in size
The number of persons immigrating plus the number of births
exceeds the number of persons emigrating plus the number of
deaths.
Population decreasing in size
The number of persons emigrating plus the number of deaths
exceeds the number of persons immigrating plus the number of
births.
Demographic Transition
Shift from high birth and death rates found in agrarian societies
to lower birth and death rates found in developed countries.
23
Epidemiologic Transition
Shift in the pattern of morbidity and mortality from infectious
and communicable diseases to chronic, degenerative diseases.
Epidemiology and the Health of the Community
Provides a key to the types of problems requiring attention
Determines the need for specific health services
25
Demographic and Social Variables
Age and sex distribution
Socioeconomic status

109. Family structure
Racial, ethnic, and religious composition
26
Variables Related to Community Infrastructure
Availability of social and health services
Quality of housing stock
Social stability (residential mobility)
Community policing
Employment opportunities
27
Health-Related Outcome Variables
Homicide and suicide rates
Infant mortality rate
Chronic and infectious diseases
Drug and alcohol abuse rates
Teen pregnancy rates
Sexually transmitted diseases
Birth rate
28
Environmental variables
Air pollution from stationary and mobile sources
Access to parks/recreational facilities
Availability of clean water
Availability of markers that supply healthful groceries

110. Number of liquor stores and fast-food outlets
Nutritional quality of foods and beverages vended to school -
children
Health Disparities
Healthy People 2010, Goal 2
“ . . . To eliminate health disparities among segments of the
population, including differences that occur by gender, race, or
ethnicity, . . .”
Healthy People 2020
“. . .To achieve health equity, eliminate disparities, and improve
the health of all groups. . .”
30
Health Disparities
Infant mortality in the U.S.
Income inequality (Gini index)
Ranges for 0 to 1
The closer the index is to one, the greater is the level of
inequality.
States with the highest Gini Scores: Tennessee, Kentucky, and
West Virginia
Epidemiology and Policy Evaluation
Using epidemiologic methodologies to evaluate public health
policies
Examples: tobacco control, needle distribution programs, ban on
plastic bags, printing of nutritional content on restaurant menus,
removal of high fat and high sugar content foods from vending

111. machines in schools, and prohibition of drivers’ use of cell
phones
32
Working of Health Services
Operations research (OR)
Program evaluation
Operations Research (OR)
The study of the placement and optimum utilization of health
services in a community
Contribution of epidemiology to OR is the development of
research designs, analytic techniques, and measurement
procedures
34
Examples of OR
Coordination of programs for the developmentally disabled
Studies of health care utilization
Residential care facilities
35
Program Evaluation
Uses epidemiologic tools to determine how well a health
program meets certain stated goals

112. 36
Epidemiology and Program Evaluation
Methods for selecting target populations
Design of instruments for data collection
Delimitation of types of health-related data
Methods for assessment of healthcare needs
37
Epidemiology and Disease Etiology
Applications include:
Search for causes
Individual risks
Specific clinical concerns
38
Causality in Epidemiologic Research
Epidemiologic research is the subject of criticism.
Many conflicting studies
Henle-Koch postulates are not relevant to many contemporary
diseases.
Multivariate causality
Risk Factors Defined
Due to the uncertainty of “causal” factors the term risk factor is
used.

113. Definition: exposure that is associated with a disease
Example of a risk factor: smoking.
40
Risk Factors Defined (cont’d)
Three Criteria for Risk Factors
The frequency of the disease varies by category or value of the
factor, e.g., light smokers vs. heavy smokers.
The risk factor precedes onset of the disease.
The observation must not be due to error.
41
Modern Concepts of Causality: 1964 Surgeon General’s Report
Five criteria for causality
Strength of association
Time sequence
Consistency upon repetition
Specificity
Coherence of explanation
42
Modern Concepts of Causality: Sir Austin Bradford Hill
Hill expanded the list of criteria to include:
Biologic gradient
Plausibility
Experiment
Analogy

114. 43
Study of Risks to Individuals
Etiologic study designs used
Case-control
Cohort
44
Case-Control Design
A type of design that compares persons who have a disease
(cases) with those who are free from the disease (controls).
This design explores whether differences between cases and
controls result from exposures to risk factors.
45
Cohort Design
A group of people free from a disease is assembled according to
a variety of exposures.
The group (cohort) is followed over a period of time for
development of disease.
46

115. How Results Impact Clinical Decisions
The following considerations determine a study’s influence:
Criteria of causality
Relevance to each patient
Size of the risk
Public health implications
Individual vs. population
47
Enlargement of the Clinical Picture of Disease
Cases of a new disease often the most dramatic cases
Need to survey a complete population
Example of a “new” disease—Legionnaires’ disease
Prevention of Disease
Research is applied to identify where in a disease’s natural
history effective intervention might be implemented.
The natural history of disease refers to the course of disease
from its beginning to its final clinical end points.
Natural History of Disease
Prepathogenesis--before agent reacts with host
Pathogenesis--after agent reacts with host
Later stages include development of active signs and symptoms.
Clinical end points are: recovery, disability, or death.
50

116. Primary Prevention as a General Concept
Occurs during prepathogenesis phase
Includes health promotion and specific protection against
diseases
51
Primordial Prevention
Concerned with minimizing health hazards in general
Examples include improvement of:
Economic conditions
Social conditions
Behavioral conditions
Cultural patterns of living
Primary Prevention as a Specific Concept
Involves specific protection against disease-causing hazards
Examples:
Utilization of specific dietary supplements
Immunizations
Educational campaigns against unintentional injuries
Primary Prevention: Active and Passive
Active
Necessitates behavior change on the part of the subject
Examples: Vaccinations and wearing protective devices
Passive
Does not require any behavior change
Examples: Fluoridation of public water and vitamin
fortifications of milk and bread products

117. 54
Secondary Prevention
Occurs during pathogenesis phase
Designed to reduce the progress of disease
Examples are screening programs for cancer and diabetes.
55
Tertiary Prevention
Takes place during late pathogenesis
Designed to limit disability from disease
Also directed at restoring optimal functioning (rehabilitation)
Examples include: physical therapy for stroke patients, halfway
houses for alcohol abuse recovery, and fitness programs for
heart attack patients.
56
Chapter 8
Experimental Study Designs
Learning Objectives (abridged)
State how study designs compare with respect to validity of
causal inference
Distinguish between a controlled experiment and a quasi -
experiment

118. Describe the scope of intervention studies
Define the term controlled clinical trials and give examples
Explain the phases in testing a new drug or vaccine
Learning Objectives (abridged)
Discuss blinding and crossover in clinical trials.
Define what is meant by community trials.
Discuss ethical aspects of experimentation with human subjects.
True Experimental Studies
Most convincing for conferring evidence of associations
between risk factors and outcomes
Manipulation of study factor and randomization of subjects
An example is a randomized clinical trial.
Women’s Health Initiative
Hormone Replacement Therapy (HRT)
Epidemiologic studies had shown that HRT use had significant
benefits against coronary heart disease.
Clinical trials had failed to demonstrate any benefit.
Large body of epidemiologic research had observed that women
who took HRT had elevated risks of breast cancer.
Women’s Health Initiative
Hormone Replacement Therapy (HRT)
To resolve the question of risks versus benefits of HRT, a
clinical trial was conducted.
Demonstrated that:
the epidemiologic findings on cancer were generally accurate
the benefits on cardiovascular disease had been overestimated
Results
Use of HRT decreased 40%-80% after the trial was stopped

119. Quasi-Experiment/Community Trial
Ranked immediately below controlled experiments in rigor
Investigator is unable to randomly allocate subjects to the
conditions.
There may be contamination across the conditions of the study.
Intervention Studies
An investigation involving intentional change in some aspect of
the status of subjects
Used to test efficacy of preventive or therapeutic measures
Manipulation of the study factor and randomization of study
subjects
Intervention Studies
Two categories:
Clinical trials (focus on the individual)
Community trial or community intervention (focus on the group
or community.
NOTE: Controlled clinical trials may be conducted both at the
individual and community levels.
Clinical Trials: Definition
A research activity that involves the administration of a test
regimen to humans to evaluate its efficacy and safety
Wide variation in usage:

120. The first use of the term was for studies in humans without any
control treatment
Now denotes a rigorously designed and executed experiment
involving RANDOM ALLOCATION of test and control
treatments
Characteristics of Clinical Trials
Carefully designed and rigidly enforced protocol
Tightly controlled in terms of eligibility, delivery of the
intervention, and monitoring out outcomes
Duration ranges from days to years
Participation is generally restricted to a highly selected group
of individuals.
Characteristics of Clinical Trials
Once subjects agree to participate, they are randomly assigned
to one of the study groups, e.g., intervention or control
(placebo)
History of Clinical Trials
In 1537, Ambroise Paré applied experime ntal treatment for
battlefield wounds.
East India Shipping Company (1600) found that lemon juice
protected against scurvy.
James Lind (1747) used the concurrently treated control group
method.
History of Clinical Trials
Edward Jenner’s efforts to develop a smallpox vaccine in the
late 18th century

121. Most recent historical developments include the use of
multicenter trials.
Instrumental in the development of treatments for infectious
diseases and recently in chronic diseases that are of
noninfectious origin
Prophylactic and Therapeutic Trials
A prophylactic trial evaluates the effectiveness of a substance
that is used to prevent disease; it can also involve a prevention
program.
A therapeutic trial involves the study of curative drugs or a new
surgical procedure to improve the patient’s health.
Outcomes of Clinical Trials
Referred to as clinical end points
May include rates of disease, death, or recovery
The outcome of interest is measured in the intervention and
control arms of the trial to evaluate efficacy--these must be
measured in a comparable manner.
Examples of Clinical Trials
Medical Research Council Vitamin Study—studied role of folic
acid in preventing neural tube defects.
South Bronx, NY, STD Program—evaluated effectiveness of
education efforts to prevent spread of sexually transmitted
diseases (STDs).
Blinding (Masking)
To maintain the integrity of a study and reduce the potential for
bias, the investigator may utilize one of two popular
approaches:
Single-blind design: subject unaware of group assignment

122. Double-blind design: Neither subject nor experimenter is aware
of group assignment
Phases of Clinical Trials
Before a vaccine, drug, or treatment can be licensed for general
use, it must go through several stages of development.
This lengthy process requires balance to:
protect the public from a potentially deleterious vaccine
satisfy the urgent needs for new vaccines
Stages in the Development of A Vaccination Program
Pre-licensing evaluation of vaccine
Phase I trials: Safety of adult volunteers
Phase II trials: Immunogenicity and reactogenicity in the target
population.
Phase III trials: protective efficacy
Post-licensing evaluation
Safety and efficacy of vaccine
Disease surveillance
Serologic surveillance
Measurement of vaccine coverage
Phase IV Trials
There can be more than three phases in a clinical trial.
Phase IV trials involve post-marketing research to gather more
information about risks and benefits of a drug.
Randomization
Method of choice for assigning subjects to the treatment or
control conditions of a clinical trial.
Non-random assignment may cause mixing of the effects of the
intervention with differences (e.g., demographic) among the

123. participants of the trial.
Crossover Designs
Any change of treatment for a patient in a clinical trial
involving a switch of study treatments
In planned crossovers a protocol is developed in advance, and
the patient may serve as his or her own control.
Unplanned crossovers exist for various reasons, such as
patient’s request to change treatme nt.
Ethical Aspects of Human Experimentation
Benefits must outweigh risks.
Ethical issues:
Informed consent
Withholding treatment known to be effective
Protective the interests of the individual patient
Monitoring for side effects
Deciding when to withdraw a patient
Reporting the Results of Clinical Trials
The CONSORT Statement is a protocol that guides the reporting
of randomized trials by providing a 22-item checklist and a
flowchart.
Summary of Clinical Trials
Strengths:
Provide the greatest control over:
the amount of exposure
the timing and frequency of exposure
the period of observation
Ability to randomize reduces the likelihood that groups will
differ significantly.

124. Summary of Clinical Trials (cont’d)
Limitations:
Artificial setting
Limited scope of potential impact
Adherence to protocol is difficult to enforce
Ethical dilemmas
Community Trials
Community intervention trials determine the potential benefit of
new policies and programs
Intervention: Any program or other planned effort designed to
produce changes in a target population
Community refers to a defined unit, e.g., a county, state, or
school district
Community Trials (cont’d)
Start by determining eligible communities and their willingness
to participate
Collect baseline measures of the problem to be addressed in the
intervention and control communities
Use a variety of measures, e.g., disease rates, knowledge,
attitudes, and practices
Community Trials (cont’d)
Communities are randomized and followed over time
Outcomes of interest are measured
Examples of Community Trials
North Karelia Project
Minnesota Heart Health Program