Managerial Epidemiology

  

Managerial Epidemiology:   Week 2

Critical Reflection Paper: Chapters 4 &5

Objective: To censoriously reveal your understanding of the readings and your ability to apply them to your Health care Setting. 

ASSIGNMENT GUIDELINES (10%):

Students will frowningly analyze the readings from Chapter 4 and 5 in your textbook. This assignment is premeditated to help you valuation, analysis, and apply the readings to your Health Care setting as well as become the groundwork for all of your outstanding assignments.

You need to read the article (in the additional weekly reading resources localize in the Syllabus and also in the Lectures link) assigned for week 1 and develop a 2 page paper reflecting your understanding and ability to apply the readings to your Health Care Setting. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA format when referring to the selected articles and include a reference page.

  

EACH PAPER SHOULD INCLUDE THE   FOLLOWING:

1. Introduction (25%) Provide a ephemeral summary of the denotation (not a description) of each Chapter and articles you read, in your own words.

2. Your Critique (50%)

What is your reaction to the content of the articles?

What did you absorb about Descriptive and analytic epidemiology?

What did you acquire about the quality and utility of epidemiologic data?

Did these Chapter and articles change your thoughts epidemiologic data sources and its weaknesses? If so, how? If not, what remained the same?

3. Conclusion (15%)

Transiently recapitulate your thoughts & assumption to your critique of the articles and Chapter you read. How did these articles and Chapters impact your thoughts on the difference between secular trends and cohort effects?

Evaluation will be based on how clearly you respond to the above, in particular:

a) The clarity with which you critique the articles;

b) The depth, scope, and organization of your paper; and,

c) Your conclusions, including a description of the impact of these articles and Chapters on any Health Care Setting.

ASSIGNMENT DUE DATE:

The assignment is to be electronically posted no later than noon on Saturday, January 18, 2020. 

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

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

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)

Descriptive Studies and

Epidemiologic Hypotheses

• Hypotheses–theories tested by gathering

facts that lead to their 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)

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.

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.

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

Cross-sectional Studies

•

Survey

s 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

Trends by Age Sub

group

• Childhood to early adolescence

– 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.

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 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

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.

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.

• 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

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)

• 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.

• 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

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.

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.

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.

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)

Nativity and Migration

• Nativity–Place of origin of the

individual

• Categories are foreign born and

native born.

• Nativity and migration are related.

Impact of Migration

• Importation of “Third World” disease by

immigrants from developing countries

– 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

Socioeconomic Status

• 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

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

Hollingshead and Redlich

• Studied association of socioeconomic

status and mental illness

• Classified New Haven, Connecticut,

into five social classes based on

occupational prestige, education, and

address

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.

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.

• Downward drift hypothesis—Persons

with severe mental disorders move to

impoverished areas.

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

Characteristics of Place

• Types of place comparisons:

– International

– Geographic (within-country) variations

– Urban/rural differences

– Localized occurrence of disease

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.

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

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

Standard Metropolitan

Statistical Areas (SMSAs)

• Established by the U.S. Bureau of

the Census to make regional and

urban/rural comparisons in disease

rates

Metropolitan Statistical Areas

(MSAs)

• Provide a distinction between

metropolitan and nonmetropolitan

areas by type of residence,

industrial concentration, and

population concentration

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

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.

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

• Environmental factors

– Example: chemical agents linked to cancer

Characteristics of Time

• Cyclic fluctuations

• Point epidemics

• Secular time trends

•

Clustering

– Temporal

– Spatial

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

• 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.

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.

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

Chapter 5

Sources of Data for Use in

Epidemiology

Learning Objectives

• Discuss criteria for assessing the quality

and utility of epidemiologic data

• Indicate privacy and confidentiality issues

that pertain to epidemiologic data

• Discuss the uses, strengths, and

weaknesses of various epidemiologic data

sources

Criteria for the Quality and

Utility of Epidemiologic Data

• Nature of the data

• Availability of the data

• Completeness of population

coverage

– Representativeness

– Generalizability (external validity)

– Thoroughness

• Strengths versus limitations

Nature of the Data

• Refers to the source of data, e.g.,

vital statistics, case registries,

physicians’ records, surveys of the

general population, or hospital and

clinic cases.

• Will affect the types of statistical

analyses and inferences that are

possible.

Availability of the Data

• Refers to investigator’s access to

data.

• For example, medical records and

other data with personal identifiers

may not be used without patients’

consent.

Completeness of Population

Coverage

• Representativeness—the degree to which

a sample resembles a parent population.

• Generalizability (external validity)— ability

to apply findings to a population that did

not participate in the study.

• Thoroughness—the care taken to identify

all cases of a given disease.

Strengths versus Limitations

• The utility of the data for various

types of epidemiologic research.

• Factors inherent in the data may limit

their usefulness.

– Incomplete diagnostic information.

– Case duplication.

Online Sources of Epidemiologic

Data
• Online bibliographic databases include

MEDLINE, TOXLINE, and commercial

databases.

• National Library of Medicine’s PubMed®

– MEDLINE is the main part of PubMed®

– Premier source of health-related literature

• TOXLINE—keyed to toxicology and includes

information on drugs and chemicals

Selected Internet Addresses

• American Public Health Association—

http://www.apha.org

• Centers for Disease Control and

Prevention—http://www.cdc.gov

• PubMed®—

http://www.ncbi.nlm.nih.gov/sites/entr

ez

Confidentiality

• Privacy Act of 1974

– Prohibits the release of confidential data
without the consent of the individual

• Freedom of Information Act

– Mandates the release of government
information to the public, except for personal
and medical files

• The Public Health Service Act

– Protects confidentiality of information
collected by some federal agencies, e.g.,
NCHS

The HIPAA Privacy Rule
• Refers to the Health Insurance Portability and

Accountability Act of 1996

• Sections of HIPAA “…require the Secretary of

HHS to publicize standards for the electronic

exchange, privacy and security of health

information…”

• Categories of protected health information

pertain to individually identifiable data re:

– The individual’s physical and mental health

– Provision of health care to the individual

– Payment for provision of health care

Data Sharing

• Refers to the voluntary release of

information by one investigator or

institution to another for the purpose of

scientific research.

• Can enhance data quality and increase

knowledge from research.

• Key issue is the primary investigator’s

potential loss of control over information.

Record Linkage

• Joining data from two or more

sources, e.g., employment records

and mortality data.

• Applications include genetic research,

planning of health services, and
chronic disease tracking.

Statistics Derived from the

Vital Registration System

• Mortality statistics

• Birth statistics: certificates of birth

and fetal death.

Mortality Statistics

• Mortality data are nearly complete, as
most deaths in the U.S. and other
developed countries are unlikely to be
unreported.

• Death certificates include demographic
information about the deceased and cause
of death (immediate cause and
contributing factors).

Limitations of Mortality Data

• Certification of cause of death.

– For example, in an elderly person with

chronic illness, exact cause of death may be

unclear.

• Lack of standardization of diagnostic criteria.

• Stigma associated with certain diseases, e.g.,
AIDS, may lead to inaccurate reporting.

Limitations of Mortality Data

(cont’d)

• Errors in coding by nosologist

• Changes in coding

– Revisions in the (ICD) International

Classification of Disease.

– Sudden increases or decreases in a

particular cause of death may be due to
changes in coding.

Birth Statistics: Certificates of Birth

and of Fetal Death

• Birth certificate includes information that
may affect the neonate, such as
congenital malformations, birth weight,
and length of gestation.

• Sources of unreliability:
– Mothers’ recall of events during pregnancy

may be inaccurate.

– Conditions that affect neonate may not be
present at birth.

Birth Statistics (cont’d)

• Varying state requirements for fetal death

certificates.

• Both types of certificates have been used

in studies of environmental influences

upon congenital malformations.

• Both provide nearly complete data.

Reportable

Disease Statistics

• Federal and state statutes require health care

providers to report those cases of diseases

classified as reportable and notifiable.

– Include infectious and communicable

diseases that endanger a population, e.g.,

STDs, measles, foodborne illness.

Limitations of Reportable

Disease Statistics

• Possible incompleteness of population

coverage.

– For example, asymptomatic persons

would not seek treatment.

• Failure of physician to fill out required

forms.

• Unwillingness to report cases that carry a
social stigma.

Screening

Survey

s

• Conducted on an ad hoc basis to identify

individuals who may have infectious or

chronic diseases. Examples: breast

cancer screenings, health fairs.

• Clientele are highly selected.

– Individuals who participate are concerned

about the particular health issue.

Multiphasic Screening

• Administration of 2 or more screening

tests during a single screening program

• Ongoing screening programs often are

carried out at worksites.

• Potential biases from worker attrition

• Data can be useful for research on

occupational health problems.

• Data may not contain etiologic information.

Disease Registries
• Registry–a centralized database for collection

of data about a disease

• Coding algorithms are used to maintain patient
confidentiality.

• Applications of registries:

– Patient tracking

– Identification of trends in rates of disease

– Case-control studies

• Example: SEER program

Surveillance, Epidemiology, and

End Results (SEER) Program

• Conducted by the National Cancer

Institute (NCI)

• Collects cancer data from different cancer

registries across the U.S.

• Provides information about trends in

cancer incidence, mortality, and survival

Morbidity Surveys of the

General Population

• Morbidity surveys collect data on the

health status of a population group.

• Obtain more comprehensive information

than would be available from routinely

collected data

• Example:

National Health Interview

Survey

National Health Survey

• Authorized under the National Health
Survey Act of 1956 to obtain information
about the health of the U.S. population.

• Refers generically to a group of surveys
and not a single survey.

• In response to the Act, the National Center
for Health Statistics (NCHS) conducts
three separate and distinct programs.

NCHS Survey Programs

• National Health Interview Survey
(NHIS)

• Health Examination Survey

(HES)

• Various surveys of health resources
– National Hospital Discharge Survey

– National Ambulatory Medical Care
Survey

National Health Interview

Survey (NHIS)

• General household health survey of the

U.S. civilian noninstitutionalized

population

• Studies a comprehensive range of

conditions such as diseases, injuries,

disabilities, and impairments

Health Examination Survey

(HES)

• Provides direct information about morbidity
through examinations, measurements, and
clinical tests

– Identifies conditions previously unreported or

undiagnosed

– Provides information not previously available

for a defined population

• Now known as the Health and Nutrition
Examination Survey (HANES)

Behavioral Risk Factor

Surveillance System (BRFSS)

• Collects data on behaviorally related

phenomena

– Behavioral risks for chronic diseases

– Preventive activities

– Healthcare utilization

• The largest telephone survey in the world

California Health Interview

Survey (CHIS)

• Provides information on the health and
demographic characteristics of California
residents

• Uses telephone survey methods

• Topics include

– Physical and mental health

– Health behaviors

– Health insurance coverage and utilization

• Conducted on a continuing basis

Insurance Data

• Sources include:

– Social Security–provides data on disability

benefits and Medicare.

– Health insurance–provides data on those

who receive care through a prepaid medical

program.

– Life insurance–provides information on

causes of mortality; also provides results of

physical examinations.

Limitations of Insurance Data

• Data may not be representative of
entire population, as the uninsured
are excluded.

Clinical Data Sources

• Hospital data

• Diseases treated in special clinics

and hospitals

• Data from physicians’ practices

Hospital Data

• Consists of both inpatient and outpatient
data

• Deficiencies of data:

– Not representative of any specific
population

– Different information collected on each
patient

– Settings may differ according to social
class of patients; e.g., specialized
clinics, emergency rooms

Diseases Treated in Special

Clinics and Hospitals

• Data cannot be generalized because

patients are a highly selected group.

• Case-control studies can be done with

unusual and rare diseases.

– However, it is not possible to

determine incidence and prevalence

rates without knowing the size of the

denominator.

Data from Physicians’

Practices

• Limited application due to:

– Confidentiality of patient data

– Highly selected group of patients

– Lack of standardization of information

collected

• Useful for the purposes of:

– Verification of self-reports

– Source of exposure data

Absenteeism Data

• Records of absenteeism from work or
school

• Possible deficiencies:

– Data omit people who neither work nor

attend school.

– Not all people who are ill take time off.

– Those absent are not necessarily ill.

• Useful for the study of rapidly spreading
conditions

School Health Programs

• Provide information about

immunizations, physical exams, and self-

reports of illness

• Have been used in studies of

intelligence, mental retardation, and

disease etiology

• Paffenbarger, et al. used information

from health records of college students

to track causes of chronic diseases.

Morbidity Data from the
Armed Forces

• Reports from physicals, hospitalizations, and
selective service examinations

• Data have been used for:

– Studies of disease etiology.

• Study of twins serving in Korean War or WWII to

determine influence of “nature and nurture” on

cause of disease.

– Studies investigating genetic factors in
obesity

Other Data Sources Relevant

to Epidemiologic Studies

• U.S. Bureau of the Census publications:

– Statistical Abstract of the United States

– County and City Data Book

– Decennial Censuses of Population and

Housing

– Historical Statistics of the United States,

Colonial Time to 1970

U.S. Bureau of the Census

• Provides information on the general, social, and

economic characteristics of the U.S. population

• U.S. Census is administered every 10 years.

– Attempts to account for every person and his

or her residence

– Characterizes population according to sex,

age, family relationships, and other

demographic variables