The United States Cancer Statistics dataset is the official source of federal cancer statistics. It is collated by the Center for Disease Control and Prevention (CDC), National Cancer Institute (NCI), and North American Association of Central Cancer Registries (NAACCR). This dataset contains statistics for cancer incidence and mortality rates in the United States from 1999 - 2016, and is broken down by states and leading cancer sites.
Leading Cancer Site- Leading cancer sites are the cancer types with the highest incidence in the United States. The cancer site is the organ system in which the cancer develops.
Year- This dataset contains information for each year from 1999 - 2016.
Mortality Age-Adjusted Rate- The mortality is the number of deaths due to a particular type of cancer. The age-adjusted rate is calculated using age distribution ratios from the year 2000, in order to reduce the confounding effect of age. The rate is displayed per 100,000 individuals in the population.
Incidence Age-Adjusted Rate- The incidence is the number of diagnoses of a particular type of cancer in living people. This metric reflects the number of people currently living with a type of cancer. The age-adjusted rate is calculated using age distribution ratios from the year 2000, in order to reduce the confounding effect of age. The rate is displayed per 100,000 individuals in the population.
This dataset can be generated by visiting this link (https://wonder.cdc.gov/cancermir-v2016.HTML), and entering the following queries: Group by- Leading cancer sites, And By- State, And By- Year
This dataset contains information regarding the 5-year relative survival rates of various cancers for different racial populations in the United States. It was collected by the U.S. Cancer Statistics Working Group (USCS), based on cases reported from 2001-2015.
Cancer Type- The cancer type is the organ system of the body in which the cancer develops.
5-Year Relative Survival Rate (%)- The 5-year relative survival rate is the percentage of cancer patients who have not died from their cancer, five years after their diagnosis.
Race- The dataset reports survival rates for three races- White, Black, and Other. The “Other” category contains Asian/Pacific Islanders, Indian Health Service-linked (IHS), and Alaskan Natives.
This dataset can be accessed by exporting the “5-year Relative Survival (%) by Cancer Type, By Race, Both Sexes” table at this link (https://gis.cdc.gov/Cancer/USCS/DataViz.html).
This dataset contains information regarding the societal costs of cancer in terms of economic impact and loss of life, and the amount of research funding that is allocated to each type of cancer by the U.S. National Cancer Institute (NCI).
Incidence- The incidence is the number of diagnoses of a particular type of cancer in living people. This metric reflects the number of people currently living with a type of cancer, in the year 2010.
Mort.- The mortality is the number of deaths due to a particular type of cancer, in the year 2010.
US Gov’t YLL (thousands)- YLL (years of life lost) is used to weigh the severity of a type of cancer by the age at which a person is killed. It is calculated by subtracting the age at death from the life expectancy at the age bracket of the person who is deceased. For example, if a man at age 40 dies of cancer, and had an estimated life expectancy of 74 years based on his age bracket, the YLL would be 34 years. The US Gov’t YLL data field was calculated by summing the YLL of all cancer patients in the United States who died in 2007. It is reported in units of thousands.
WHO DALY (thousands)- DALY (disability-adjusted life years) is another method of quantifying the health burden of cancer. It represents the lost quality of life due to non-lethal disabilities, and translates these effects into YLL equivalents. The conversion of quality of life to YLL is often considered subjective; however, the DALY metric remains highly used. In this dataset, statistics from the World Health Organization were used to calculate DALY for the United States in 2004. It is reported in units of thousands.
Medicare Spend $ (millions)- Medicare payment data from 2004 was used to estimate the 5-year cost of treatment in the United States for each type of cancer. It is reported in units of millions of dollars.
National spend $ (billions)- National spending is an estimation of the national expenditures for cancer care in the year 2006. The expenditures include public and private medical expenses for cancer treatment. It is reported in units of billions of dollars.
Lost prod. (billions)- Lost productivity was used to estimate the economic burden of cancer in the United States. It was calculated using the “present value of lifetime earnings” (PVLE) of all individuals that died from cancer in 2010. PVLE takes demographic differences in earning potential into account by weighing working-age individuals more heavily than the elderly and males higher than females. It also accounts for racial differences in earnings. Thus, it essentially quantifies loss of wages due to cancer deaths, and is reported in units of billions of dollars.
NCI Funding (millions)- NCI (National Cancer Institute) funding is the amount of money allocated to cancer research in the year 2010. It is reported in units of millions of dollars.
AYLL- AYLL (average years of life lost) is calculated by dividing the YLL value by the number of deaths for each cancer. It was calculated for the United States using data from 2007, and it is reported in units of years.
YLLPI- YLLPI (years of life lost per individual) is calculated by dividing the YLL value by the number of new cases for each cancer. Unlike AYLL, which measures years of life lost due to individuals who die, YLLPI includes the chances of successful treatment, which would result in a YLL value of “0.” Thus, cancers with high cure rates are expected to have lower YLLPI than cancers for which therapies are less effective. YLLPI was calculated for the United States using data from 2007, and it is reported in units of years.
This dataset can be found by viewing Table 1 in this link (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411479/#B1).
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