Download Epidemiology

Principles of Epidemiology
Dona Schneider, PhD, MPH, FACE
E J Bloustein School of Planning and Public Policy
Rutgers University, NJ, USA
About the Author

Dona Schneider
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Known Risk Factors for Cancer

Smoking

Reproductive factors

Dietary factors

Socioeconomic status

Obesity


Exercise
Environmental
pollution

Ultraviolet light

Radiation

Prescription Drugs

Electromagnetic fields



Occupation
Genetic
susceptibility
Infectious agents
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Preliminary Topics

Data sources and limitations for cancer
epidemiology

How much cancer is occurring?

How does occurrence vary within the
population?

How do cancer rates in your area compare
to that in other areas?
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Data sources and limitations for
cancer epidemiology
Review U.S. Census, U.S. Vital
Statistics, SEER and NJCR data
Race Categories in the Census 1860-2000
1860
1870
1900
1970
20002
White
White
White
White
White
Black
Black
Negro or Black
Quadroon1
Quadroon
Black of Negro
decent
Black, African American, or
Negro
Chinese
Chinese
Indian (Amer.)
American Indian or Alaska Native
Japanese
Filipino
Japanese
Filipino
Asian Indian
Korean
Native Hawaiian
Vietnamese
Guamanian or Chamorro
Samoan
Other Asian
Other Pacific Islander
Some other race
Chinese
Chinese
Indian
Indian
Japanese Japanese
Korean
Hawaiian
Other
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Office of Management and Budget (OMB)
Revision of Statistical Policy Directive No. 15,
Race and Ethnic Standards for Federal
Statistics and Administrative Reporting

Revised racial and ethnic standards
(effective as of the 2000 decennial
census) have 5 minimum categories for
data on race and 2 for ethnicity

Other Federal programs should adopt
standards no later than January 1, 2003
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OMB Race Categories

American Indian or Alaska Native
A person having origins in any of the original people
of North and South America (including Central
America) and who maintain tribal affiliation or
community attachment

Asian
A person having origins in any of the original people
of the Far East, Southeast Asia of the Indian
subcontinent including for example, Cambodia,
China, India, Japan, Korea, Malaysia, Pakistan, the
Philippine Islands, Thailand and Vietnam
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OMB Race Categories



(continued)
Black or African American
A person having origins in any of the black racial
groups of Africa. Terms such as “Haitian” or
“Negro” can be used in addition to “Black or African
American”
Native Hawaiian or Other Pacific Islander
A person having origins in any of the original
peoples of Hawaii, Guam, Samoa or other Pacific
Islands
White
Persons having origins in any of the original peoples
of Europe, the Middle East or North Africa
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Census Data

Changes to the Race Question in the 2000
Census:

The Asian and Pacific Islander (API) category
was split:
a) Asians
b) Native Hawaiian and Other Pacific Islanders (NHOPI)

The category American Indian, Eskimo, Aleut
(AIEA) was changed to American Indian or
Alaskan Native (AIAN)

Respondents could select more than one race.
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U.S. Census Bureau
http://www.census.gov/
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Vital Statistics

Maintained by the National Center for Health
Statistics
(http://www.cdc.gov/nchs/nvss.htm)

States report the following to NCHS:




Birth data (Natality)
Death data (Mortality)
Marriage data (no longer collected)
Divorce data (no longer collected)
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CDC Wonder
http://wonder.cdc.gov/
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Registries for Morbidity Data

New Jersey Cancer Registry
http://www.state.nj.us/health/cancer/statistics.htm

SEER: Surveillance, Epidemiology, and End
Results
http://seer.cancer.gov/
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Data Limitations

Little data is available at the local level

Problem of small numbers

Data may not be collected uniformly
(race category differences, etc.)

People are mobile

Cancer has a long lag time
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How much cancer is occurring?
Understand incidence rates and prevalence
Measuring Epidemiological Outcomes
Relationship between any two numbers
(e.g. males / females)
Ratio
Proportion
Rate
A ratio where the numerator is
included in the denominator
(e.g. males / total births)
A proportion with the specification of time
(e.g. deaths in 2000 / population in 2000)
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Definitions

Incidence is the rate of new cases of a
disease or condition in a population at risk
during a time period

Prevalence is the proportion of the
population affected
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Incidence
Number of new cases during a time period
Incidence =
Population at risk during that time period

Incidence is a rate

Calculated for a given time period (time interval)

Reflects risk of disease or condition
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Prevalence
Prevalence =
Number of existing cases
Total number in the population at risk

Prevalence is a proportion

Point Prevalence: at a particular instant in time

Period Prevalence: during a particular interval of
time (existing cases + new cases)
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Prevalence = Incidence  Duration
Prevalence depends on the rate of occurrence
(incidence) AND the duration or persistence of
the disease
At any point in time:

More new cases (increased risk) yields more
existing cases

Slow recovery or slow progression increases
the number of affected individuals
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Incidence/Prevalence Example
For male residents of Connecticut:

The incidence rate for all cancers in 1982


431.9 per 100,000 per year
The prevalence of all cancers on January 1, 1982

1,789 per 100,000 (or 1.8%)
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Proportional cancer incidence by gender, US 2000
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How does occurrence vary
within the population?
Understand measures of
association and difference
Outcome Measures

Compare the incidence of disease among
people who have some characteristic with
those who do not

The ratio of the incidence rate in one group to
that in another is called a rate ratio or relative
risk (RR)

The difference in incidence rates between the
groups is called a risk difference or
attributable risk (AR)
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Calculating Outcome Measures
Outcome
Exposure
Disease
(cases)
No Disease
(controls)
Exposed
A
B
IE = A / (A+B)
Not Exposed
C
D
IN = C / (C+D)
Incidence
Relative Risk = IE / IN
Attributable Risk = IE - IN
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Lung Cancer
Exposure
Smoker
Non-smoker
Yes
No
Total
Incidence
70
300
370
70/370 = 189 per 1000
30
700
730
30/730 = 41 per 1000
100
1,000
1,100
Relative Risk = IE / IN = 189 / 41 = 4.61
Attributable Risk = IE - IN = 189 - 41 = 148 per
1000
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Relative Risk = IE / IN = 189 / 41 = 4.61
Attributable Risk = IE - IN = 189 - 41 = 148 per
1000

Smokers are 4.61 times more likely than
nonsmokers to develop lung cancer

148 per 1000 smokers developed lung
cancer because they smoked
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RR < 1
RR = 1
RR > 1
Risk
comparison
between
exposed and
unexposed
Risk for disease
is lower in the
exposed than in
the unexposed
Risk of disease
are equal for
exposed and
unexposed
Risk for disease
is higher in the
exposed than in
the unexposed
Exposure as a
risk factor for
the disease?
Exposure
reduces disease
Particular
risk
exposure is not a
risk factor
(Protective
factor)
Exposure
increases
disease risk
(Risk factor)
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Annual Death Rates for Lung Cancer and
Coronary Heart Disease
by Smoking Status, Males
Annual Death Rate / 100,000
Coronary Heart
Disease
Exposure
Lung Cancer
Smoker
127.2
1,000
12.8
500
RR
127.2 / 12.8 = 9.9
1000 / 500 = 2
AR
127.2 – 12.8 = 114.4
per 100,000
1000 – 500 = 500
per 100,000
Non-smoker
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Summary

The risk associated with smoking is lower for
CHD (RR=2) than for lung cancer (RR=9.9)

Attributable risk for CHD (AR=500) is much
higher than for lung cancer (AR=114.4)

In conclusion: CHD is much more common
(higher incidence) in the population, thus the
actual number of lives saved (or death averted)
would be greater for CHD than for lung cancer
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