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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 . Smoking Dietary factors Obesity Exercise Occupation Genetic susceptibility Infectious agents.

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principles of 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
About the Author
  • Dona Schneider
known risk factors for cancer
Smoking

Dietary factors

Obesity

Exercise

Occupation

Genetic susceptibility

Infectious agents

Reproductive factors

Socioeconomic status

Environmental pollution

Ultraviolet light

Radiation

Prescription Drugs

Electromagnetic fields

Known Risk Factors for Cancer
preliminary topics
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?
data sources and limitations for cancer epidemiology

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

Race Categories in the Census 1860-2000

White

White

White

White

White

Black

Black

Negro or Black

Black of Negro decent

Black, African American, or Negro

Quadroon1

Quadroon

Chinese

Chinese

Chinese

Chinese

Indian

Indian

Indian (Amer.)

American Indian or Alaska Native

Japanese

Japanese

Japanese

Japanese

Filipino

Filipino

Asian Indian

Korean

Korean

Hawaiian

Native Hawaiian

Vietnamese

Guamanian or Chamorro

Samoan

Other Asian

Other Pacific Islander

Other

Some other race

office of management and budget omb
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
omb race categories
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

omb race categories continued
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

census data
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.
u s census bureau
U.S. Census Bureau

http://www.census.gov/

vital statistics
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)
cdc wonder
CDC Wonder

http://wonder.cdc.gov/

registries for morbidity data
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/

data limitations
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
how much cancer is occurring

How much cancer is occurring?

Understand incidence rates and prevalence

measuring epidemiological outcomes
Measuring Epidemiological Outcomes

Relationship between any two numbers

(e.g. males / females)

Ratio

A ratio where the numerator is included in the denominator (e.g. males / total births)

Proportion

A proportion with the specification of time

(e.g. deaths in 2000 / population in 2000)

Rate

definitions
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
incidence
Incidence
  • Incidence is a rate
  • Calculated for a given time period (time interval)
  • Reflects risk of disease or condition

Number of new cases during a time period

Incidence =

Population at risk during that time period

prevalence
Prevalence
  • Prevalence is a proportion
  • Point Prevalence: at a particular instant in time
  • Period Prevalence: during a particular interval of time (existing cases + new cases)

Number of existing cases

Prevalence =

Total number in the population at risk

prevalence incidence duration
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
incidence prevalence example
Incidence/Prevalence Example

For male residents of Connecticut:

  • The incidence rate for all cancers in 1982
    • 431.9 per 100,000 per year
  • The prevalenceof all cancers on January 1, 1982
    • 1,789 per 100,000 (or 1.8%)
how does occurrence vary within the population

How does occurrence vary within the population?

Understand measures of association and difference

outcome measures
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 differencein incidence rates between the groups is called a risk difference or attributable risk (AR)
calculating outcome measures
Calculating Outcome Measures

Outcome

No Disease

(controls)

Exposure

Disease

(cases)

Incidence

IE = A / (A+B)

Exposed

A

B

IN = C / (C+D)

Not Exposed

C

D

Relative Risk = IE / IN

Attributable Risk = IE - IN

slide27

Lung Cancer

Exposure

Yes

No

Total

Incidence

Smoker

70

300

370

70/370 = 189 per 1000

30/730 = 41 per 1000

Non-smoker

30

700

730

100

1,100

1,000

Relative Risk = IE / IN = 189 / 41 = 4.61

Attributable Risk = IE - IN = 189 - 41 = 148 per 1000

slide28

Relative Risk = IE / IN = 189 / 41 = 4.61

  • Smokers are 4.61 times more likely than nonsmokers to develop lung cancer
  • 148 per 1000 smokers developed lung cancer because they smoked

Attributable Risk = IE - IN = 189 - 41 = 148 per 1000

annual death rates for lung cancer and coronary heart disease by smoking status males
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

Non-smoker

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

summary
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