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

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

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

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

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

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

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

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

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

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

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

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 1992-1996, age-adjusted to 1970 United States = 170 per 100,000

Age-adjusted death rates per 100,000

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

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)

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?

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

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

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

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

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

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

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