Cancer mortality rates of WHO global regions
Mortality.RdCancer mortality rates of WHO global regions.
Format
A list object of the five WHO global regions:
Aus-NZ Europe Northern Americaa list object of 18 items, each of which contains a data.frame of cancer rates (see Details)
Northern Africa - Western Asiaa list object of 18 items, each of which contains a data.frame of cancer rates (see Details)
Latin America and Caribbeana list object of 18 items, each of which contains a data.frame of cancer rates (see Details)
Asia excl. Western Asiaa list object of 18 items, each of which contains a data.frame of cancer rates (see Details)
Sub-Saharan Africaa list object of 18 items, each of which contains a data.frame of cancer rates (see Details)
Details
The list object for each region contains the 18 site-specific cancer mortality rates (esophagus, stomach, colon, liver, pancreas, lung, breast, prostate, bladder, brainCNS, thyroid, all_leukaemia, all_cancer, allsolid-NMSC, allsolid, leukaemia, allcause, survival).
Each site-specific data.frame contains variables age, male and female.
For allcause mortality data.frame contains age-specific person-year data, male_py and female_py, in addition to age, male and female.
Examples
names(Mortality) # WHO global regions
#> [1] "Aus-NZ Europe Northern America" "Northern Africa - Western Asia"
#> [3] "Latin America and Caribbean" "Asia excl. Western Asia"
#> [5] "Sub-Saharan Africa"
names(Mortality[[1]]) # Sites for which baseline mortality rates are available
#> [1] "esophagus" "stomach" "colon" "liver"
#> [5] "pancreas" "lung" "breast" "prostate"
#> [9] "bladder" "brainCNS" "thyroid" "all_leukaemia"
#> [13] "all_cancer" "allsolid-NMSC" "allsolid" "leukaemia"
#> [17] "allcause" "survival"
# Example 1: All solid cancer mortality rates for Region-1
head( Mortality[[1]]$allsolid )
#> age male female
#> 1 1 3.993729e-06 3.302123e-06
#> 2 2 1.198119e-05 9.906370e-06
#> 3 3 1.996865e-05 1.651062e-05
#> 4 4 1.946148e-05 1.625391e-05
#> 5 5 1.895432e-05 1.599719e-05
#> 6 6 1.844715e-05 1.574048e-05
tail( Mortality[[1]]$allsolid )
#> age male female
#> 95 95 0.02189662 0.01180741
#> 96 96 0.02189662 0.01180741
#> 97 97 0.02189662 0.01180741
#> 98 98 0.02189662 0.01180741
#> 99 99 0.02189662 0.01180741
#> 100 100 0.02189662 0.01180741
# Example 2: Leukaemia mortality rates for Region-3
head( Mortality[[3]]$leukaemia )
#> age male female
#> 1 1 4.066002e-06 3.390723e-06
#> 2 2 1.219801e-05 1.017217e-05
#> 3 3 2.033001e-05 1.695362e-05
#> 4 4 2.103463e-05 1.704913e-05
#> 5 5 2.173925e-05 1.714464e-05
#> 6 6 2.244387e-05 1.724015e-05
tail( Mortality[[3]]$leukaemia )
#> age male female
#> 95 95 0.0004455293 0.0002765286
#> 96 96 0.0004455293 0.0002765286
#> 97 97 0.0004455293 0.0002765286
#> 98 98 0.0004455293 0.0002765286
#> 99 99 0.0004455293 0.0002765286
#> 100 100 0.0004455293 0.0002765286
# Example 3: A;;ll;-cause mortality rates for Region-5
head( Mortality[[5]]$allcause )
#> age male female male_py female_py
#> 1 1 0.056160846 0.048467260 18761.24 18253.13
#> 2 2 0.009220674 0.007515785 18127.85 17684.38
#> 3 3 0.007183781 0.006129537 17634.31 17232.95
#> 4 4 0.005848985 0.005303171 17194.27 16823.33
#> 5 5 0.004864717 0.004698722 16794.60 16448.30
#> 6 6 0.004105606 0.004193329 16422.67 16095.09
tail( Mortality[[5]]$allcause )
#> age male female male_py female_py
#> 95 95 0.3576011 0.2754637 9.8210 23.4550
#> 96 96 0.3680964 0.2902003 6.9710 16.8780
#> 97 97 0.3780983 0.3040167 4.9220 11.9500
#> 98 98 0.3884477 0.3176900 3.4625 8.3635
#> 99 99 0.3989252 0.3305587 2.4190 5.8265
#> 100 100 0.4149599 0.3458980 1.6845 4.0590
# Example 4: plotting lung cancer mortality rates
plot_refdata( dat=Mortality, outcome="lung", leg_pos=c(0.27,0.95) )
