Guidance for Comparing Cancer Data by Geographic Region

Overview

When looking at figures that rank cancer rates by state, county, or other geographic region, some readers wonder why their community has higher rates than other communities or the national average. For example, some may worry that exposure to environmental carcinogens may be responsible, when several other explanations are more likely. Consider the following points when comparing rates by geographic region.

Difference in cancer rates

Differences among racial and ethnic populations

Some cancers have different rates for different racial and ethnic populations. For example, breast cancer incidence rates are usually higher in non-Hispanic White women than in women of other racial and ethnic populations. Prostate cancer incidence rates are higher in non-Hispanic Black men. Therefore, when comparing cancer rates across geographic regions, consider the racial makeup of the region’s population, which is determined through the statistical adjustment of rates by race and ethnicity. Presenting rates for specific racial and ethnic populations may be preferable and is more easily understood by a lay audience.

Variations in risk factors and health behaviors

Some differences in cancer rates among geographic regions may be explained by differences in known risk factors among the populations of those regions. For example, rates of lung cancer and other tobacco-associated cancers are higher in regions with a higher prevalence of smoking. Although environmental carcinogens are responsible for some cancer cases, most cases appear to be related to lifestyle factors such as smoking. Geographic variations in cancer rates are thought largely to reflect variations in these lifestyle factors.12 Additionally, many people move during their lifetime and may have been exposed to a risk factor in one community and diagnosed with cancer years later in another community.

Variations in medical care

Variations among geographic regions in medical care factors may also result in differences in cancer rates. In places where higher percentages of the population participate in cancer screening, more cancers will be diagnosed. Screening leads to earlier detection of tumors that have a better prognosis and may find tumors that grow slowly. Therefore, the cancer incidence rate only tells part of the story.

Influence of aging on cancer rates

The likelihood of being diagnosed with cancer increases with age. These rates have been adjusted for age so that geographic regions can be compared without concern that differences in their rates result from differences in the age distribution of their populations. However, this adjustment may be imperfect if the relationship between age and cancer risk is not the same for all regions.

Additional considerations

Measuring burden

The importance of cancer as a public health problem in a community is more a function of the absolute rate of cancer rather than the community's relative ranking in incidence or mortality. For example, Utah has proportionately fewer people who have ever smoked cigarettes than other states, and also has the lowest lung cancer incidence rate of any state. Nevertheless, in Utah lung cancer kills more people than any other cancer—a fact that might be overlooked if one focused only on its low incidence ranking compared with other states.

The true burden of cancer on a community's health care system and economy is determined by the number of people diagnosed with and dying of cancer—not by the age-adjusted cancer rate. So, the cancer rate in one community may seem high compared with other communities, but the number of cases is small.

Accuracy and completeness in cancer reporting and coding

U.S. Cancer Statistics incidence data are from central cancer registries that have high-quality data. However, some information about the patient or tumor may be missing or incomplete. For example, a medical record may only have a PO Box for a patient's address, so the patient is coded as living in that county when they may actually live in a different county. Also, reporting facilities may fall behind on reporting their cancer cases, and registries have varying resources to find these unreported cases. When comparing rates, especially from relatively small geographic regions, remember that each rate depends on the accurate and complete ascertainment of many details.

Random factors and cancer rates

There is some uncertainty in computed cancer rates because many factors contribute to the rates, and some factors may happen at random. Chance plays a role in determining if and when cancer develops in an individual, if the cancer is detected, if the information is entered into the cancer registry, and if the cancer leads to death. For these reasons, rates are expected to vary from year to year within a community even in the absence of a general trend. Caution is warranted when examining cancer rates for a single year, especially when the rates are based on a relatively small number of cases.

Confidence intervals

A 95% confidence interval for the rate is an interval that is expected to contain the true underlying rate 95% of the time. Confidence intervals around the observed age-adjusted rates are available to help interpret the results. Because of the variation in the population sizes and number of reported cases and deaths across geographic regions, there is more uncertainty in the incidence and death rates for some regions compared with others.

The confidence intervals provide a measure of the variability in the rates and some perspective for making comparisons. However, using overlapping confidence intervals to conclude that rates are not significantly different is not recommended. This is a conservative method because it may fail to detect significant differences more often than standard statistical hypothesis testing.

Public health importance

Another consideration when comparing differences between rates is their public health importance. For some rates, numerators and denominators are large and the standard errors are therefore small. This results in statistically significant differences that may be too small to be important for decisions related to population-based public health programs.