Mortality rates for major cancers stratified by sex and age group, expressed in micromorts per year and daily microlives.
Value
A tibble with columns: cancer_type, sex, age_group, deaths_per_100k, micromorts_per_year, microlives_per_day, family_history_rr, rank_by_sex, source_url.
References
SEER Cancer Statistics Factsheets. National Cancer Institute. https://seer.cancer.gov/statfacts/
Siegel RL, et al. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12-49.
See also
vaccination_risks(), conditional_risk(), hedged_portfolio()
Other conditional-risk:
conditional_risk(),
hedged_portfolio(),
vaccination_risks()
Examples
cancer_risks()
#> # A tibble: 43 × 10
#> cancer_type sex age_group deaths_per_100k micromorts_per_year
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Lung & Bronchus Male All ages 37.2 372
#> 2 Prostate Male All ages 19.2 192
#> 3 Colon & Rectum Male All ages 15.3 153
#> 4 Pancreas Male All ages 12.9 129
#> 5 Liver Male All ages 10.2 102
#> 6 Leukemia Male All ages 7.8 78
#> 7 Esophagus Male All ages 7.1 71
#> 8 Bladder Male All ages 6.5 65
#> 9 Non-Hodgkin Lymphoma Male All ages 5.6 56
#> 10 Multiple Myeloma Male All ages 3.3 33
#> # ℹ 33 more rows
#> # ℹ 5 more variables: microlives_per_day <dbl>, family_history_rr <dbl>,
#> # micromorts_with_family_history <dbl>, rank_by_sex <int>, source_url <chr>
cancer_risks() |> dplyr::filter(sex == "Female")
#> # A tibble: 18 × 10
#> cancer_type sex age_group deaths_per_100k micromorts_per_year
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Lung & Bronchus Female All ages 27.1 271
#> 2 Breast Female All ages 19.2 192
#> 3 Colon & Rectum Female All ages 10.8 108
#> 4 Pancreas Female All ages 9.9 99
#> 5 Ovary Female All ages 6.1 61
#> 6 Uterus Female All ages 5.3 53
#> 7 Leukemia Female All ages 4.8 48
#> 8 Liver Female All ages 4.2 42
#> 9 Non-Hodgkin Lymphoma Female All ages 3.6 36
#> 10 Multiple Myeloma Female All ages 2.1 21
#> 11 Hodgkin Lymphoma Female All ages 0.3 3
#> 12 All cancers Female All ages 128. 1281
#> 13 Lung & Bronchus Female 50-64 35 350
#> 14 Breast Female 50-64 25 250
#> 15 Colon & Rectum Female 50-64 12 120
#> 16 Lung & Bronchus Female 65-74 95 950
#> 17 Breast Female 65-74 45 450
#> 18 Colon & Rectum Female 65-74 28 280
#> # ℹ 5 more variables: microlives_per_day <dbl>, family_history_rr <dbl>,
#> # micromorts_with_family_history <dbl>, rank_by_sex <int>, source_url <chr>
cancer_risks() |> dplyr::filter(age_group == "50-64")
#> # A tibble: 7 × 10
#> cancer_type sex age_group deaths_per_100k micromorts_per_year
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 All cancers Both 50-64 125 1250
#> 2 Lung & Bronchus Male 50-64 45 450
#> 3 Prostate Male 50-64 8 80
#> 4 Colon & Rectum Male 50-64 18 180
#> 5 Lung & Bronchus Female 50-64 35 350
#> 6 Breast Female 50-64 25 250
#> 7 Colon & Rectum Female 50-64 12 120
#> # ℹ 5 more variables: microlives_per_day <dbl>, family_history_rr <dbl>,
#> # micromorts_with_family_history <dbl>, rank_by_sex <int>, source_url <chr>