Increase the proportion of cancer survivors who are living 5 years or longer after diagnosis ā€” C‑11 Data Methodology and Measurement

About the National Data


Baseline: 64.1 percent of persons with cancer were living 5 years or longer after diagnosis and were followed up to determine their status in 2014

Target: 66.2 percent

5-year observed survival rate.
5-year expected survival rate.
Target-setting method
Target-setting method details
Linear trend fitted using weighted least squares and a projection at the 50 percent prediction interval.
Target-setting method justification
Trend data were evaluated for this objective. Using historical data points, a trend line was fitted using weighted least squares and the trend was projected into the next decade. This method was used because three or more comparable data points were available, the projected value was within the range of possible values, and a projection at the 50 percent prediction interval was selected because the number of cancer survivors in the United States has been steadily growing because of advances in early detection and treatment of cancers. Therefore, the Healthy People 2030 Workgroup Subject Matter Experts viewed the target as ambitious yet achievable.


Methodology notes

2011-2015 data will be available in 2019. The methodology will be revised to reflect the use of NPCR and SEER data. The following methodology was used in Healthy People 2020: This measure is tracked with a calculation commonly referred to as the relative survival rate. The relative survival rate is calculated using a procedure whereby the observed survival rate is adjusted for expected mortality. The relative survival rate represents the likelihood that a patient will not die from causes associated specifically with the given cancer before some specified time (usually 5 years) after diagnosis.

To calculate the relative survival rate, the observed survival rate is divided by the expected survival rate. The observed survival rate is based on all causes of death; no one is excluded. Individuals lost to follow up are censored. The expected survival rate is based on life tables of surviving 5 years in the general population based on age (single year), race, sex, and year of diagnosis (1970, 1980, 1990) of the cohort of cancer patients. This calculation is used so that one does not have to depend on the accuracy and completeness of the cause of death information in order to calculate the effect of the cancer. Data for this objective are calculated based on patients diagnosed in the 5-year period immediately preceding a given year and followed up through that year. For example, the 2007 survival rates used in the Healthy People 2020 baseline are based on patients diagnosed in the 5 year period before 2007 (2002-2006) and followed up through 2007. Survival rates are from the SEER program. They are based on data from population-based registries in the SEER 17 areas (San Francisco, Connecticut, Detroit, Hawaii, Iowa, New Mexico, Seattle, Utah, Atlanta, San Jose-Monterey, Los Angeles, Alaska Native Registry, Rural Georgia, California excluding San Francisco/San Jose-Monterey/Los Angeles, Kentucky, Louisiana and New Jersey). Since the cancer registry databases are continually updated, the estimates will change annually as more cases are received. To ensure the latest data available are shown, all tracking data for this objective will be updated on an annual basis until the close of Healthy People 2030.


Comparable HP2020 objective
Modified, which includes core objectives that are continuing from Healthy People 2020 but underwent a change in measurement.
Changes between HP2020 and HP2030
This objective differs from Healthy People 2020 objective C-13 in that objective C-13 was tracked using data from the Surveillance, Epidemiology, and End Results Program (SEER), while this objective is being tracked using data from both SEER and the National Program of Cancer Registries (NPCR).

1. Because Healthy People 2030 objectives have a desired direction (e.g., increase or decrease), the confidence level of a one-sided prediction interval can be used as an indication of how likely a target will be to achieve based on the historical data and fitted trend.