The time-dependent county attributes include a socioeconomic status (SES) index and seven county-level SES attributes that were used to construct the index. The attributes and the index are estimated at various time points using data obtained from the 1990 and 2000 U.S. Decennial Census long form survey, and a series of American Community Survey (ACS) 5-year estimates from 2006 to 2023. At each time point, a SES index is constructed from these attributes using a factor analysis (Yu et al. 2014). The attributes and the index are then linked to cancer cases/deaths at the county level by matching the survey data year and diagnosis/death year. Thus, for any of the seven attributes and index, cancer cases/deaths in the same county may have different values for different years. The mapping of the SES attributes/index and diagnosis or death year are shown in Table 1.

Table 1.

Years Data Source Geographic Definition Counties
1990-1993 1990 Census 1990 All counties
1994-1995 1990 Census 1990 All counties except combined Aleutians East Borough and Aleutians West Borough, Alaska, combined Dillingham Census Area and Lake and Peninsula Borough, Alaska, combined Skagway-Hoonan-Angoon Census Area and Yakutat City and Borough, Alaska and combined La Paz County and Yuma County, Arizona
1994-1995 2000 Census 2000 Aleutians East Borough, Alaska (02013)
Aleutians West Borough, Alaska (20016)
Dillingham Census Area, Alaska (02070)
Lake and Peninsula Borough, Alaska (02164)
Skagway-Hoonan-Angoon Census Area, Alaska (02232)
Yakutat City and Borough, Alaska (02282)
La Paz County, Arizona (04012)
Yuma County, Arizona (04027)
1996-2001 2000 Census 2000 All counties
2002-2005 2000 Census 2000 All counties except Adams County, Boulder County, Jefferson County and Weld County,Colorado
2002-2005 ACS 2006-10 2010 Adams County, Colorado (08001)
Boulder County, Colorado (08013)
Broomfield County, Colorado (08014)
Jefferson County, Colorado (08059)
Weld County, Colorado (08123)
2006-2008 ACS 2006-10 2010 All counties
2009 ACS 2007-11 2010 All counties
2010 ACS 2008-12 2010 All counties
2011 ACS 2009-13 2010 All counties
2012 ACS 2010-14 2010 All counties
2013 ACS 2011-15 2010 All counties
2014 ACS 2012-16 2010 All counties
2015 ACS 2013-17 2010 All counties
2016 ACS 2014-18 2010 All counties
2017 ACS 2015-19 2010 All counties
2018 ACS 2016-20 2020 Chugach Census Area (FIPS Code:02063) and Copper River Census Area (02066) combined to create Valdez-Cordova Census Area (02261)
2019 ACS 2017-21 2020 Chugach Census Area and Copper River Census Area combined to create Valdez-Cordova Census Area
2020 ACS 2018-22 2020 Chugach Census Area and Copper River Census Area combined to create Valdez-Cordova Census Area
2021-2023 ACS 2019-23 2020 Chugach Census Area and Copper River Census Area combined to create Valdez-Cordova Census Area

Variable Descriptions

The selection of attributes for constructing the SES index is based on Yost et al. (2001). Below are the tables and formulas used to calculate the base variables used in constructing the SES score. The tables are from the 2017-2021 American Community Survey (ACS) 5 –year estimates. Similar tables were used from the 1990 and 2000 Census and the 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, and 2019-2023 ACS.

Median Household Income

Median household income is taken from table B19013: Median Household Income in the Past 12 Months (in 2023 inflation adjusted dollars). The inflation adjustment is different for each of the data sources (i.e., 2018-22 ACS median income is in 2022 inflation adjusted dollars). In addition to the median income from the original data source, median income is available for all time points adjusted to 2023 dollars. This is available as dollars and income groupings.

Median House Value

Median house value is taken from table B25077: Median Value (Dollars).

Median Gross Rent

Median gross rent is taken from table B25064: Median Gross Rent (Dollars)

Percent Below 150% Poverty

The percent below 150% of poverty line is calculated using table C17002: Ratio of Income to Poverty Level in the Past 12 Months. The formula for this is:

  • Below 150% poverty: ((C17002e02 + C17002e03 + C17002e04 + C17002e05) / C1702e01) * 100

Education Index

The education index is calculated using table B15002: Sex by Educational Attainment for the Population 25 Years and Over. The percent with less than high school graduate, high school only and more than high school are calculated, as follows:

  • Less than HS grad: ((B15002e03 + ... + B15002e10 + B15002e20 + ... + B15002e27) / B15002e01) * 100
  • HS Only: ((B15002e11+B15002e28)/B15002e01) * 100
  • More than HS grad: ((B15002e12 + ... + B15002e18 + B15002e29 + ... + B15002e35) / B15002e01) * 100

Using the percentages calculated above, the education index is calculated as follows:

  • Education Index: (Less than HS grad * 9) + (HS only *12) + (More than HS grad * 16)

Working Class

The percent working class is calculated using table C24010: Sex by Occupation for the Civilian Employed Population 16 Years and Over.  The formula for this is:

  • Working Class: ((C24010e20 + C24010e24 + C24010e25 + C24010e26 + C24010e27 + C24010e30 + C24010e34 + C24010e56 + C24010e60 + C24010e61 + C24010e62 + C24010e63 + C24010e66 + C24010e70)) / C24010e01) * 100

Percent Unemployed

The percent of persons ages 16 and over who are unemployed is calculated using table B23025: Employment Status for the Population 16 Years and Over. The percent unemployed is calculated for civilians in the labor force. Persons in the armed forces or not in the labor force are not included in the calculation. The formula used is:

  • Unemployed: (B23025e05 / B23025e03) * 100

Rural-Urban Continuum Codes

Rural-Urban Continuum Codes were developed by the United States Department of Agriculture (USDA).

Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan (metro) counties by the population size of their metro area, and nonmetropolitan (nonmetro) counties by degree of urbanization and adjacency to a metro area or areas.

The mapping of the original Rural-Urban Continuum Codes to the collapse categories available in the time dependent county attributes are shown in Tables 2, 3, and 4.

Table 2. RUCC 1993 (1990-1997)

Category Area
0: Central counties of metro areas of 1 million population or more
1: Fringe counties of metro areas of 1 million population or more
Counties in metropolitan areas greater than or equal to 1,000,000 population
2: Counties in metro areas of 250,000 to 1 million population Counties in metropolitan areas of 250,000 to 1,000,000 population
3: Counties in metro areas of fewer than 250,000 population Counties in metropolitan areas of less than 250,000  population
4: Urban population of 20,000 or more, adjacent to a metro area
6: Urban population of 2,500 to 19,999, adjacent to a metro area
8: Completely rural or less than 2,500 urban population, adjacent to a metro area
Nonmetropolitan counties adjacent to a metropolitan area
5: Urban population of 20,000 or more, not adjacent to a metro area
7: Urban population of 2,500 to 19,999, not adjacent to a metro area
9: Completely rural or less than 2,500 urban population, not adjacent to a metro area
Nonmetropolitan counties not adjacent to a metropolitan area

Table 3. RUCC 2003 (1998-2007), RUCC 2013 (2008-2017 All Counties, 2018-2023 Connecticut Counties)

Category Area
1: Counties in metro areas of 1 million population or more Counties in metropolitan areas greater than or equal to 1,000,000 population
2: Counties in metro areas of 250,000 to 1 million population Counties in metropolitan areas of 250,000 to 1,000,000 population
3: Counties in metro areas of fewer than 250,000 population Counties in metropolitan areas of less than 250,000  population
4: Urban population of 20,000 or more, adjacent to a metro area
6: Urban population of 2,500 to 19,999, adjacent to a metro area
8: Completely rural or less than 2,500 urban population, adjacent to a metro area
Nonmetropolitan counties adjacent to a metropolitan area
5: Urban population of 20,000 or more, not adjacent to a metro area
7: Urban population of 2,500 to 19,999, not adjacent to a metro area
9: Completely rural or less than 2,500 urban population, not adjacent to a metro area
Nonmetropolitan counties not adjacent to a metropolitan area

Table 3. RUCC 2023 (2018-2023 All Counties Except Connecticut Counties)

Category Area
1: Counties in metro areas of 1 million population or more Counties in metropolitan areas greater than or equal to 1,000,000 population
2: Counties in metro areas of 250,000 to 1 million population Counties in metropolitan areas of 250,000 to 1,000,000 population
3: Counties in metro areas of fewer than 250,000 population Counties in metropolitan areas of less than 250,000  population
4: Urban population  of 20,000 or more, adjacent to a metro area
6: Urban population  of 5,000 to  20,000, adjacent to a metro area
8: Urban population  of fewer than 5,000, adjacent to a metro area
Nonmetropolitan counties adjacent to a metropolitan area
5: Urban population  of 20,000 or more, not adjacent to a metro area
7: Urban population  of 5,000 to  20,000, not adjacent to a metro area
9: Urban population  of fewer than 5,000, adjacent to a metro area
Nonmetropolitan counties not adjacent to a metropolitan area

In addition to the mapped categories in the tables above, the following categories are used to classify areas that are not covered by the RUCC.

  • 88: Unknown-Alaska/Hawaii State/not official USDA Rural-Urban Continuum code
  • 99: Unknown/not official USDA Rural-Urban Continuum code

The Rural-Urban Continuum Codes for 2023 are not available for counties in Connecticut. The data are only available for planning regions. To be consistent with geographies in the incidence and death data the value for 2013 are used for 2008-2023 for Connecticut. For more information about using Rural-Urban Continuum Codes, go to Rural-Urban Continuum Codes in SEER*Stat.

References

Yost K, Perkins C, Cohen R, Morris C, Wright W (2001) Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control 12(8):703–711. doi:10.1023/a:1011240019516.

Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. Cancer Causes & Control. 2014;25(1):81-92.