Managerial Epidemiology

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

• 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

Trends by Age Subgroup

• 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