1. How do cancer rates in your area compare to those in other areas?
Understand the use of standardized rates, specific rates, and the limitations of computer mapping

2. Rates Crude Specific Adjusted
Summary rate of the actual number of observed events in a population over a given time period (e.g. all cancer deaths in 2000)
Crude
Rates for specific segments/groups of the population (e.g. sex, age, race, cause of death, cancer site)
Specific
Rates are standardized to a control population
Adjusted

3. Crude Rates Estimates the burden of disease in a population
Not useful for making comparisons between groups or examining changes over time, because it depends largely on population structure

4. Specific Rate
Important because outcomes may be profoundly affected by factors such as age, race, and gender
More precise indicator of risk than a crude rate as it controls for a particular characteristic of interest
Allows for comparisons between strata or between groups

5. Examples of Specific Rates
Age specific rates
Gender specific rates
Race specific rates
Cause specific rates
Site specific rates

6. Lung Cancer Deaths by Age Group, United States, 1995
Age (years)
Population
Lung Cancer Deaths
Age-Specific Lung Cancer Death Rate
Per 100,000
5-14
38,134,488 11
11 / 38,134,488 = 0.03
15-24
35,946,635 41
41 / 35,946,635 = 0.11
25-34
40,873,139
303
303 / 40,873,139 = 0.74
35-44
42,467,719
2,709
2,709 / 42,467,719 = 6.38
45-54
31,078,760
12,356
12,356 / 31,078,760 = 39.76
Total
188,500,741
15,420
xxx
Cause Specific Rate = (15,420/188,500,741) x 100,000 = 8.18 / 100,000

7. Adjusted Rate
Specific rates are standardized to a control population and are summarized to produce an adjusted rate
Used to compare rates of entire populations taking into account differences in population structure (e.g., age, gender, race or other variables)
Adjusted rates can be compared if they are calculated using the same standard population

8. Expected Number of Deaths
Creating a cause-specific, age-adjusted death rate using direct standardization
Age
Cancer Deaths
Population at risk
ASR
1980 U.S.
Standard Population
Expected Number of Deaths
(1) / (2) = (3)
(1)
(2)
(4)
(1) / (2) x (4) = (5)
0-18 5
5,000
60,500,000
19-64 10
25,000
140,300,000
65+
100
15,000
25,700,000
Total
115
45,000
xxx
226,500,000
Crude Rate
(115 / 45,000) x 1000
2.56 per 1,000

9. Expected Number of Deaths
Creating a cause-specific, age-adjusted death rate using direct standardization
Age
Cancer Deaths
Population at risk
ASR
1980 U.S.
Standard Population
Expected Number of Deaths
(3) x (4) = (5)
(1)
(2)
(1) / (2) = (3)
(4)
0-18 5
5,000
1.00 per 1000
60,500,000
60,500
19-64 10
25,000
0.40 per 1000
140,300,000
56,120
65+
100
15,000
6.67 per 1000
25,700,000
171,419
Total
115
45,000
xxx
288,039
226,500,000
Crude Rate
(115 / 45,000) x 1000
2.56 per 1,000
Age-Adjusted Rate
(288,039 / 226,500,000) x 1000
1.27 per 1,000

10. Comparing Crude and Age-Adjusted Rates
If crude rate decreases after adjustment, the study population is older than the standard population
If crude rate increases after adjustment, the study population is younger than the standard population

11. Standard Population
By convention, SEER uses the 1970 US standard population

12. Cancer Death Rates by State per 100,000, 2000
Utah
122
Wisconsin
163
Rhode Island
178
Hawaii
133
Florida
166
South Carolina
178
Colorado
142
Oregon
166
Alabama
179
New Mexico
146
Alaska
167
New Jersey
179
Idaho
148
Texas
168
Ohio
180
Arizona
155
New York
169
Arkansas
181
Nebraska
155
Oklahoma
170
New Hampshire
181
North Dakota
155
Vermont
172
Tennessee
181
South Dakota
155
Michigan
173
Mississippi
182
California
156
Georgia
175
Maryland
184
Minnesota
156
North Carolina
175
Nevada
184
Wyoming
157
Missouri
176
West Virginia
184
Kansas
159
Pennsylvania
177
Maine
185
Montana
159
Virginia
177
Kentucky
192
Iowa
160
Illinois
178
Louisiana
193
Washington
162
Indiana
178
Delaware
195
Connecticut
163
Massachusetts
178
Dist. Of Col.
212
Average annual mortality , age-adjusted to 1970
United States = 170 per 100,000

13. Age-adjusted death rates per 100,000

14. Source: CDC Wonder: Unpublished Mortality Data, 1998
Rates are per 100,000 and age-adjusted to the 1940 U.S. Standard Population
* These rates should be interpreted with caution as they represent 20 or fewer deaths

15. Cautions in Comparing Rates
Precision: Rates calculated from an area with a small population are subject to a large amount of variation from year to year
Comparability: Rates are affected by differences in population structure (e.g., a county with more older women may have higher rates for breast cancer than a county with more younger women)

16. Advanced Topics
What types of investigations address cancer etiology and control?
How do we evaluate whether cancer studies are valid?
How do we assess whether associations between cancer and risk factors are causal?
How much of the morbidity and mortality from cancer might be prevented by interventions?

17. What types of investigations address cancer etiology and control?
Understand case-control, cohort, and intervention studies

18. Descriptive Studies (to generate hypotheses)
Case-Reports / Series
Cross-Sectional Studies (Prevalence Studies) measure exposure and disease at the same time
Ecological Studies (Correlational Studies) use group data rather than data on individuals. These data cannot be used to assess individual risk – to do so is to commit Ecological Fallacy

19. Analytic Studies (to test hypotheses)
Observational Studies
Cohort Studies
Case-Control Studies
Experimental Studies
Randomized Control Trials (RCT / Clinical Trials)

20. Cohort Study Design
A group of people (cohort) without disease are identified and characterized by an exposure
Group is followed forward over a period of time to observe the development (incidence) of the disease of interest

21. Single Sample Cohort Study Design
Time
Diseased
Disease-Free
Cohort
Exposed
Not Diseased
Target Population
Diseased
Not Exposed
Not Diseased

22. Multi-Sample Cohort Study Design
Time
Diseased
Study Cohort
Exposed
Not Diseased
Diseased
Control Cohort
Not Exposed
Not Diseased

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