A Brief Introduction to
Epidemiology - IV
( Overview of Vital Statistics &
Demographic Methods)
Betty C. Jung, RN, MPH, CHES
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Learning Objectives
To
understand the how vital
statistics and demographic data are
used in Public Health
To understand the measures of
mortality, fertility, morbidity that
are based on vital statistics
To understand the basis for Rate
Adjustment
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Performance Objectives
Basic
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understanding of how to
use the most commonly available
health statistics to quantify
disease in Public Health Practice
Basic understanding of the most
common vital statistical
measures encountered in
Practice
Introduction
Demographic
data and vital
statistics are useful tools in:
– Determining a community’s health
status
– Deciding what’s the best way for
providing health services
– Planning a public health program
– Evaluating a program’s effectiveness
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Demographic Data
Demographic data include
those variables that describe
the characteristics of a
population (i.e., population
size and how it changes over
time)
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Demographic Variables
Population composition include:
Age
Sex
Income
Occupation
Health services use
Geographic location
Geographic density
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Vital Statistics (Events)
Include:
Births
Deaths
Marriages
Divorces
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Sources of Vital Statistics &
Demographic Data in the U.S.
Census
Registration
of Vital
Events
Morbidity Surveys
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Demographic Data:
United States Census
 The
United States conducts a decennial census
(every 10 years) since 1790. Each household
and resident is enumerated (counted).
 Person info: sex,age,race,marital status, place
of residence, and relationship to or position as
head of household
 A systematic sample of households provides:
income, housing, number of children born,
education, employment status, means of
transportation to work, and occupation.
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Demographic Data:
United States Census
Census tables are published for the
entire U.S., each state, urbanized
areas (Metropolitan Statistical Areas
[MSAs]), counties, cities,
neighborhoods (census tracts), and
city blocks.
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Demographic Data:
Annual Registration of Vital Events
In the U.S., state laws require that all
vital events be registered.
Birth certificates serve as proof of
citizenship, age, birthplace and
parentage.
Death Certificates - required as burial
documents and in settlement of
estates and insurance claims.
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Demographic Data:
US Vital Statistics Data
Vital Statistics of the United States annual - detailed tables of vital events
by various demographic characteristics
and major geographic subdivision.
Data on marriages and divorces are
collected and published in a separate
volume.
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Demographic Data:
US National Death Index
Prepared by NCHS - a nationwide,
computerized index of death records
compiled from each state’s vital statistics
offices.
Allows researchers to decide if persons in
their studies have died. Includes death
certificate number, state person died in
and date of death.
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Demographic Data:
U.S. Morbidity Surveys
 Morbidity
data (i.e., prevalence of
disease)
– Communicable disease reports are shared
through CDC’s Morbidity and Mortality
Weekly Reports (MMWR)
– More serious diseases are well reported (i.e.,
cholera,plague,yellow fever, rabies, paralytic
polio)
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Demographic Data:
U.S. Sources of Morbidity Data
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Reportable diseases
National Health Survey
Hospital records data
Industrial hygiene records
School health records
Medical care subgroups (i.e.,prepaid health insurance
plans)
Chronic Disease Registries (i.e., tumor registries)
Insurance industry data
Vital Statistics Rates, Ratios, and
Proportions
Three rates used in vital statistics:
Crude rates - computed for an entire
population
Specific rates - consider differences among
subgroups, computed by age, race, sex or other
variables.
Adjusted (standardized) rates - to make valid
summary comparisons between two or more
groups with different age (or other)
distributions.
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Measures of Mortality
Each rate is a measure of the relative frequency
of deaths that occurred in a given population
over a specific time period (time at risk).
Population size is usually defined as the
population at midyear (July 1).
These measures estimate the population at risk
(a+b)/time(t) of one year. If this convention
cannot be met, then the calculation should
really be considered a “proportion” rather
than a rate.
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Measure of Mortality:
Annual Crude Death Rate
 Universally
used as generalized indicator
of a population’s health.
 May not be truly reflective without
accounting for age, race, or sex.
 Example:
State, Yr - population: 5000; deaths: 25
Crude Death Rate = 25/5000 x 1000
5 deaths per 1000 per year
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Measure of Mortality:
Age-Specific Death Rate

Defined as the number of deaths in a specific age group
in a calendar year, divided by the population of the
same age group on July 1 of that year, the quotient
multiplied by 1000.
 Example:
Country, Yr - age group: 25-34 yrs; population:
5,000,000; deaths: 200,000
Age-specific death rate= 200,000/5,000,000 x 1000
= 40 deaths per 1000 population per year for age
group 25-34
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Measure of Mortality:
Cause-Specific Death Rate
 Defined
as the number of deaths assigned to a
specific cause in a calendar year, divided by the
population on July of that year, the quotient
multiplied by 100,000
 Example:
Country, Yr - cause: accidents; population: 5,000,000;
deaths: 4,000
Cause-specific death rate= 4,000/5,000,000 x 100,000
= 80 accidental deaths per 100,000 population per
year
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Measure of Mortality:
Proportional Mortality Ratio
 Defined
as the number of deaths assigned to a
specific cause in a calendar year, divided by the
total number of deaths in that year, the
quotient multiplied by 100
 Example:
Country, Yr - total deaths from all causes: 1,500,000;
deaths from cancer: 675,000
Proportional mortality ratio= 675,000/1,500,000 x 100
= 45% of total deaths per year from cancer
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Measure of Mortality:
Infant Mortality Rate
 Defined
as the number of deaths of persons age
0-1 in a calendar year, divided by the number
of live births in that year, quotient multiplied
by 1000
 Example:
State, Yr - live births: 325,000; infant deaths: 1,750
Infant mortality= 1,750/325,000 x 1000
= 5.4 infant deaths per 1000 live births per year
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Measure of Mortality:
Maternal Mortality Ratio*
 Defined
as the number of deaths assigned to
puerperal causes (i.e., childbearing) in a
calendar year divided by the number of live
births in that year, the quotient multiplied by
100,000.
 Example:
Country, Yr - deaths due to puerperal causes: 275;
live births: 1,750,000.
Maternal mortality ratio: 275/1,750,000 x 100,000
= 15.71 maternal deaths per 100,000 live births per
year
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Measure of Mortality:
Neonatal Mortality Proportion
 Defined
as the number of deaths of neonates
(infants <28 days of age) in a calendar year,
divided by number of live births in that year,
the quotient multiplied by 1000
 Example:
State, Yr - deaths at <28 days=2,750; live births:
325,000
Neonatal mortality proportion= 2,750/325,000 x
1000
= 8.5 neonatal deaths per 1000 live births
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Measure of Mortality:
Fetal Death Ratio
 Defined
as the number of fetal deaths in
a calendar years, divided by the number
of live deaths in that year, the quotient
multiplied by 1000.
 Example:
State, Yr - fetal deaths: 2,450; live births: 525,000
Fetal death ratio = 2,450/525,000 x 1000
= 4.7 fetal deaths per 1000 live births
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Measure of Mortality:
Perinatal Mortality Proportion
 Defined
as the number of fetal plus neonatal
deaths, divided by the number of live births
plus fetal deaths, the quotient multiplied by
1000
 Example:
State, Yr - fetal deaths: 3,250; neonatal deaths: 5,750;
live births: 475,000
Perinatal mortality proportion= 3,250+5,750/475,000+
3,250 x 1000
= 18.8 perinatal deaths per 1000 fetal deaths plus
live births
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Measure of Fertility:
Crude Birthrate
 Defined
as the number of live births in a
calendar year, divided by the population at
July 1 of that year, the quotient multiplied by
1000
 Example:
State, Yr - live births: 250,000; population: 30,000,000
Crude birthrate= 250,000/30,000,000 x 1000
= 8.3 live births per 1000 population per year
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Measure of Fertility:
General Fertility Rate
 Defined
as the number of live births in a
calendar year, divided by the number of
women ages 15-44 at midyear, quotient
multiplied by 1000
 Example:
Country, Yr - live births: 7,500,000; number of women
ages 15-44: 35,000,000
General fertility rate= 7,500,000/35,000,000 x 1000
= 214.3 live births per 1000 women ages 15-44 per
year
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Measure of Morbidity:
Incidence Rate
 Defined
as the number of newly reported cases
of a given disease in a calendar year, divided by
the population on July 1 of that year, the
quotient multiplied by either 1000, 100,000, or
1,000,000 (whatever’s convenient).
 Example:
State, Yr - new cases of AIDS reported: 5,250;
population: 35,000,000
Incidence rate: 5,250/35,000,000 x 100,000
= 15 new AIDS cases per 100,000
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Measure of Morbidity:
Prevalence Proportion
 Defined
as the number of existing cases of a
given disease at a given time, divided by the
population at that time, the quotient multiplied
by 1000, 100,000, or 1,000,000 (whatever’s
convenient)
 Example:
Country, Yr - number of men alive with AIDS: 3,750;
population: 15,000,000 men
Prevalence proportion= 3,750/15,000,000 x 100,000
= 25 AIDS cases per 100,000 men
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Measure of Morbidity:
Case-Fatality Proportion
 Defined
as the number of deaths assigned to a
given cause in a certain period, divided by
number of cases of the disease reported during
the same period, the quotient multiplied by
100.
 Example:
Country, Yr - report number of male AIDS cases:
45,000; deaths from the disease: 37,000.
Case-fatality proportion: 37,000/45,000 x 100
= 82.2% mortality among reported cases of AIDS
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Adjustment of Rates
(or, Rate Adjustment)
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Adjusting, or standardizing, rates is used to make valid
comparisons between populations that may differ in
some significant way (i.e., age distribution).
Standardized rates have no meaning in isolation, since
adjusted rates are artificial.
Depending on type of data - there are two methods to
adjust rates - direct (preferred) and indirect.
The numerical values of the adjusted rates depend on
the choice of the standard population.