1. Principles of Epidemiology
Dona Schneider, PhD, MPH, FACE
E J Bloustein School of Planning and Public Policy
Rutgers University, NJ, USA

2. About the Author Dona Schneider
Throughout her research on maternal and child health, Dr. Schneider has conducted studies evaluating data quality and how it can affect interpretation of study results. This study of concurrence between maternal reports and medical records on children’s chronic health conditions is the latest of her data quality evaluation papers.
Links:
Dona Schneider’s home page at the E J Bloustein School of Planning and Public Policy, Rutgers University, NJ, USA
Environmental and Occupational Health Sciences Institute, New Brunswick, NJ, USA

3. 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

4. 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?

5. Data sources and limitations for cancer epidemiology
Review U.S. Census, U.S. Vital Statistics, SEER and NJCR data

6. Race Categories in the Census 1860-2000
1870
1900
1970
20002
White
White
White
White
White
Black
Black
Black of Negro decent
Negro or Black
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
Source: 200 Years of U.S. Census Taking: Population And Housing Questions U.S. Department of Commerce. U.S. Bureau of the Census.
1 In 1890, mulatto was defined as a person who was three-eighths to five-eighths black. A quadroon was one-quarter black and an octoroon one-eighth black.
2 Categories printed in the 2000 Census Dress Rehearsal questionnaire.
Guamanian or Chamorro
Samoan
Other Asian
Other Pacific Islander
Other
Some other race

7. 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

8. 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

9. 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

10. 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.

11. U.S. Census Bureau

12. Vital Statistics
Maintained by the National Center for Health Statistics (
States report the following to NCHS:
Birth data (Natality)
Death data (Mortality)
Marriage data (no longer collected)
Divorce data (no longer collected)

13. CDC Wonder

14. Registries for Morbidity Data
New Jersey Cancer Registry
SEER: Surveillance, Epidemiology, and End Results

15. 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

16. How much cancer is occurring?
Understand incidence rates and prevalence

17. 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

18. 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

19. Incidence Incidence = Incidence is a rate
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

20. Prevalence Prevalence is a proportion
Number of existing cases
Prevalence =
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)

21. 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

22. 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%)

23. Proportional cancer incidence by gender, US 2000

24. How does occurrence vary within the population?
Understand measures of association and difference

25. 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)

26. Calculating Outcome Measures
Exposure
Disease
(cases)
No Disease
(controls)
Incidence
IE = A / (A+B)
Exposed A B
IN = C / (C+D)
Not Exposed C D
Relative Risk = IE / IN
Attributable Risk = IE - IN

27. Lung Cancer Relative Risk = IE / IN = 189 / 41 = 4.61
Exposure
Yes No
Total
Incidence
Smoker 70
300
370
70/370 = 189 per 1000
Non-smoker 30
700
730
30/730 = 41 per 1000
100
1,100
1,000
Relative Risk = IE / IN = 189 / 41 = 4.61
Attributable Risk = IE - IN = = 148 per 1000

28. 148 per 1000 smokers developed lung cancer because they smoked
Relative Risk = IE / IN = 189 / 41 = 4.61
Attributable Risk = IE - IN = = 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

29. 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 risk
(Protective factor)
Particular exposure is not a risk factor
Exposure increases disease risk (Risk factor)

30. Coronary Heart Disease
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 = per 100,000
1000 – 500 = 500 per 100,000

31. 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