Overlevelsestavler, Grønland

(opdateret den 11. april 2025)

Indledning

Det konsoliderede Befolkningsregnskab, opgøres for hele landet og for definerede geografiske inddelinger. Befolkningsregnskabets tabeller indeholder alle nødvendige oplysninger til at estimere befolkningens dødshyppigheder og mange afledte demografiske mål.

Da datagrundlaget således er offentligt og frit tilgængeligt, er formålet med denne beskrivelse, at gøre anvendte beregningsmetoder reproducerbare.

De demografiske hændelser i Befolkningsregnskabet kan fordeles efter fødselsårgang og kalenderår. Desuden anvendes i variabel, som angiver, om hændelsen i det enkelte kalenderår sker før eller efter årets fødselsdag. Med denne viden kan hændelserne opgøres i såkaldte Lexis elementar-trekanter, som muliggør dødelighedsberegninger, hvor hændelser valgfrit kan opgøres enten ud fra:

a-gruppe) Alder og kalenderår ved hændelse eller
b-gruppe) Fødselsårgang og kalenderår ved hændelse eller
c-gruppe) Alder og fødselsårgang ved hændelse

Når overlevelse beregnes for små befolkninger er datagrundlaget så spinkelt, at der er stor usikkerhed om de beregnede mål. Derfor beregnes overlevelse samlet for flere år. Beregninger for Grønland har traditionelt alene har været beregnet for 5 løbende kalenderår.

De store fødselsårgange i slutningen af 1960’erne, blev afløst af halvt så store fødselsårgange i begyndelsen af 1970’erne. Det har betydet at befolkningens aldersfordeling siden markant ændres med få års mellemrum.

5-års gennemsnit kan derfor let skjule udviklingstendenser i dødeligheden, hvorfor den dannede Statistikbank-tabel: BEDLTALL beregnes med både, 1, 2 og 5 års basis.

I den kode som danner dette dokument kan køn, alder og fødested samt beregningsperiode tilpasses, men som udgangspunkt, vises her beregning af dødelighed for : mænd, født i Grønland i 65-års alderen for 5-års perioden2020:2024

En aldersbetinget dødshyppighed angiver, hvor mange personer der dør fra én fødselsdag til den næste.

For at estimere de aldersbetingede dødshyppigheder beregnes først køns- og aldersbetingede dødskvotienter, som en given periodes antal dødsfald i forhold til risikobefolkningens gennemlevede tid.

Metoden svarer til den Danmarks Statistik anvendte tidligere og som fx er beskrevet i Befolkningens bevægelser, bilag 2, side 225



R-pakken MortalityLaws af Marius D. Pascariu med fuld dokumentation i hans phd fra 2018) anvendes til at beregne overlevelsestabeller.

Beregningsresultater gemmes i en px-fil, klar til at blive vist i Statistikbanken eller lokalt med Pxwin. Se den svenske Statistikmyndighed SCB for mere information.

PX-filen dannes med Grønlands Statistiks R-pakke pxmake (på CRAN). Se StatisticsGreenland.github.io/pxmake for mere info & kilde kode.


Beregningsmetoder

Traditionelt har det været lettest at finde statistiske tabeller, hvor antal dødsfald i et kalenderår har været fordelt efter køn og alder ved død. Sat i forhold til en årlig opgørelse af befolkningens køn- og aldersfordeling kan en overlevelsestavle med afledte dødelighedsmål estimeres.

Alder vil typisk være opgjort på 1 eller 5 år intervaller og hver alder vil bestå af oplysninger flere fødselsårgange, og der ses en relativ stor korrektionspost i Befolkningsregnskabet.

Disse a-grupper vil være retvisende for store befolkninger, hvor der er lille forskel på fødselsårgangenes størrelse og dødelighed.

For Grønland er der imidlertid meget stor forskel på fødselsårgangenes størrelse. Derfor beregnes dødelighed mere retvisende ved b-grupper (fødselsårgang), hvor korrektionsposten over en årrække går mod 0. Korrektioner er en følge af forsinkede indberetninger til de administrative registre.

Nedenfor er detaljerede beregninger gennemgået for hhv a-, b-grupper og c-grupper (for 0-årige).

(A) Alder & kalenderår, Lexis firkanter (a-grupper)

Lexis firkanter (alder ved død og kalenderår) er den mindst datakrævende beregningsmetode og metoden som ofte anvendes ved beregninger for større befolkninger.

Dødskvotienten beregnes som (døde i alder x)/(middelfolketal i alder x) for den samme tidsperiode.

Fra den almindelige befolkningsstatistik genfindes disse tal:


Alder 65 i slutningen af året=Alder 66 i begyndelsen af det efterfølgende år

Figur 2 Lexis firkanter, : mænd, født i Grønland

Tabel 1. Sum (rødt areal) - alder: 65, fødselsårgang: 1954 - 1959
Fødested: Grønland
Døde Ind-vandring Ud-vandring Befolkning (primo) Befolkning (ultimo) Middelfolketal Korrektioner
Total 38 6 15 1,391 1,219 1,305.0 −125
2020 8 1 1 235 199 217.0 −28
2021 3 1 4 268 229 248.5 −33
2022 10 1 4 277 254 265.5 −10
2023 9 2 2 291 262 276.5 −20
2024 8 1 4 320 275 297.5 −34



Dødshyppighed (alder: 65 ) = 38 / 1305 = 0.0291188

Udvandringshyppighed (alder: 65 ) = 15 / 1305 = 0.0114943




(B) Fødselsårgang og kalenderår, Lexis vertikale parallelogrammer (b-grupper)

Med Lexis vertikale parallelogrammer beregnes dødshyppigheder fra samme fødselsårgang(e), indenfor et kalenderår, som så strækker sig over 2 aldre. Korrektionsposten er her tilnærmelsesvis lig nul, pga. forskinkede indberetninger fra det administrative befolkningsregister.

Dødskvotienterne beregnes som summen af døde delt med summen af middelfolketallet.

Dødshyppigheden for alder x beregnes som gennemsnittet af 2 dødskvotienter som (dødskvotient i fødselsårgang x + dødskvotient i fødselsårgang x+1) for det samme tids span.



Figur 3 Lexis, vertikale parallelogrammer, : mænd, født i Grønland 

# install.packages('pak')
pak::pak("StatisticsGreenland/statgl")

library(tidyverse)
library(statgl)

# Download event data 
events <- map_df(2020:2024, ~ mutate(statgl_fetch(
  statgl_url("BEXCALCF"),
  taar=.x,
  faar=px_all(),
  fsted=px_all(),
  ttype=c("I","O","D"),
  trekant=c("0","1"),
  sex=px_all(),
  .val_code = TRUE, .col_code = TRUE)
)) %>% 
  mutate(fsted=ifelse(fsted=="A","S",fsted),
         age=strtoi(taar)-strtoi(faar)-strtoi(trekant)) %>% 
  filter(age>=0 & age < 100) %>% 
  group_by(fsted,trekant,ttype,age,taar,faar,sex) %>% 
  summarise(value=sum(value), .groups='drop') %>% 
  arrange(fsted,trekant,ttype,age,taar,faar,sex) %>% 
  mutate(value=ifelse(is.na(value),0,value))
Tabel 2a. B1 (rødt areal) - Alder: 65/66, Fødselsårgang: 1954 - 1958
Fødested: Grønland
period Døde Ind-vandring Ud-vandring Befolkning (primo) Befolkning (ultimo) Middelfolketal Korrektioner
Total 45 6 21 1,391 1,329 1,360.0 −2
2020 6 1 1 235 229 232.0 0
2021 10 1 5 268 254 261.0 0
2022 10 1 4 277 262 269.5 −2
2023 12 2 6 291 275 283.0 0
2024 7 1 5 320 309 314.5 0
Tabel 2b. B2 (grønt areal) - Alder: 64/65, Fødselsårgang: 1955 - 1959
Fødested: Grønland
period Døde Ind-vandring Ud-vandring Befolkning (primo) Befolkning (ultimo) Middelfolketal Korrektioner
Total 29 4 19 1,528 1,485 1,506.5 1
2020 7 0 3 277 268 272.5 1
2021 3 0 4 284 277 280.5 0
2022 4 2 5 298 291 294.5 0
2023 9 0 3 332 320 326.0 0
2024 6 2 4 337 329 333.0 0
Tabel 2c. Dødskvotienter (Dødsfald/Middelfolketal)
Beregnet: fra totaler i B1 & B2
src Døde Befolkning (primo) Befolkning (ultimo) Middelfolketal Deathrate
B1 45 1,391 1,329 1,360.0 0.0331
B2 29 1,528 1,485 1,506.5 0.0192
Tabel 2d. Udvandringskvotient (Udvandring/Middelfolketal)
Beregnet: fra totaler i B1 & B2
src Udvandring Befolkning (primo) Befolkning (ultimo) Middelfolketal Emigrationrate
B1 21 1,391 1,329 1,360.0 0.0154
B2 19 1,528 1,485 1,506.5 0.0126



Dødshyppighed (alder: 65 ) = sum(0.0330882, 0.0192499) / 2 = 0.0261691

Udvandringshyppighed (alder: 65 ) = sum(0.0154412, 0.012612) / 2 = 0.0140266




(C) Fødselsårgang, første leveår. Lexis horisontale parallelogrammer

I første leveår beregnes døds- og udvandringshyppighederne direkte fra fødsel til alder 1 år, som gennemsnit for 1 eller flere fødselsårgange.




Figur 4 Lexis, horisontale parallelogrammer, : mænd, født i Grønland

Tabel 3. Beregning af død- og udvandringshyppighed for 0-årige (5 år)
cohort Dødsfald Udvandrede Levendefødte Dødshyppighed Udvandringshyppighed
Total 6 138 1800 0.0033 0.0767
2019 2 35 395 0.0051 0.0886
2020 0 36 384 0.0000 0.0938
2021 1 38 325 0.0031 0.1169
2022 2 17 346 0.0058 0.0491
2023 1 12 350 0.0029 0.0343




Beregning af overlevelse

Til at beregne overlevelsestavlen anvendes R-pakken MortalityLaws, med funktionen LifeTable, der ud fra fx de beregnede aldersbetingede dødshyppigheder danner restlevetider mm.

Fra R-pakkens dokumentation:

Compute Life Tables from Mortality Data

Description
Construct either a full or abridged life table with various input
choices like: death counts and mid-interval population estimates 
(Dx, Ex) or age-specific death rates (mx) or death probabilities (qx)
or survivorship curve (lx) or a distribution of deaths (dx). If one of
these options is specified, the other can be ignored. The input data
can be an object of class: numerical vector, matrix or data.frame.

Usage
LifeTable(x, Dx = NULL, Ex = NULL,mx = NULL,
          qx = NULL,
          lx = NULL,dx = NULL,sex = NULL,
          lx0 = 1e5,ax  = NULL)


library(MortalityLaws)
LifeTable(x, qx = d_dshyppighed, lx0 = 1000)

Ovenfor blev dødshyppigheden beregnet for 65-årige mænd, født i Grønland:

Dødshyppighed (alder: 65 ) = sum(0.0330882, 0.0192499) / 2 = 0.0261691,

og den ses i dette udsnit af den beregnede overlevelsestavle.

Tabel 4: Overlevelsestavle. Udsnit
beregnet med MortalityLaws
x.int x mx qx ax lx dx Lx Tx ex
[63,64) 63 0.02079611 0.02058136 0.4982670 748.6987 15.40924 740.9673 10151.582 13.55897
[64,65) 64 0.02102954 0.02080996 0.4982476 733.2894 15.25973 725.6328 9410.614 12.83342
[65,66) 65 0.02651758 0.02616908 0.4977902 718.0297 18.79017 708.5931 8684.981 12.09557
[66,67) 66 0.02950112 0.02907021 0.4975416 699.2395 20.32704 689.0260 7976.388 11.40723
[67,68) 67 0.03048136 0.03002149 0.4974599 678.9125 20.38197 668.6697 7287.362 10.73388
Tabel 4: Overlevelsestavle. Mænd
beregnet med MortalityLaws
x.int x mx qx ax lx dx Lx Tx ex
[0,1) 0 0.0033389013 0.0033333333 0.4997218 1000.000000 3.33333333 998.332406 68125.994147 68.1259941
[1,2) 1 0.0018379952 0.0018363072 0.4998468 996.666667 1.83018613 995.751293 67127.661741 67.3521690
[2,3) 2 0.0005217261 0.0005215900 0.4999565 994.836481 0.51889681 994.577010 66131.910448 66.4751562
[3,4) 3 0.0002597065 0.0002596728 0.4999784 994.317584 0.25819724 994.188480 65137.333438 65.5095862
[4,5) 4 0.0001000050 0.0001000000 0.4999917 994.059386 0.09940594 994.009683 64143.144958 64.5264718
[5,6) 5 0.0005076142 0.0005074854 0.4999577 993.959981 0.50442019 993.707749 63149.135276 63.5328751
[6,7) 6 0.0007607471 0.0007604578 0.4999366 993.455560 0.75548107 993.077772 62155.427527 62.5648796
[7,8) 7 0.0002530044 0.0002529724 0.4999789 992.700079 0.25112575 992.574511 61162.349755 61.6121133
[8,9) 8 0.0002694328 0.0002693966 0.4999775 992.448954 0.26736233 992.315266 60169.775244 60.6275769
[9,10) 9 0.0002694328 0.0002693966 0.4999775 992.181591 0.26729030 992.047940 59177.459977 59.6437794
[10,11) 10 0.0001000050 0.0001000000 0.4999917 991.914301 0.09919143 991.864704 58185.412037 58.6597169
[11,12) 11 0.0001000050 0.0001000000 0.4999917 991.815109 0.09918151 991.765518 57193.547333 57.6655334
[12,13) 12 0.0001000050 0.0001000000 0.4999917 991.715928 0.09917159 991.666341 56201.781815 56.6712505
[13,14) 13 0.0005457026 0.0005455537 0.4999545 991.616756 0.54098023 991.346242 55210.115474 55.6768682
[14,15) 14 0.0010975758 0.0010969736 0.4999085 991.075776 1.08718400 990.532085 54218.769232 54.7069866
[15,16) 15 0.0021020948 0.0020998869 0.4998248 989.988592 2.07886410 988.948796 53228.237147 53.7665157
[16,17) 16 0.0034453504 0.0034394219 0.4997129 987.909728 3.39783840 986.209833 52239.288351 52.8786051
[17,18) 17 0.0039217564 0.0039140763 0.4996732 984.511890 3.85345467 982.583903 51253.078518 52.0593799
[18,19) 18 0.0050950334 0.0050820758 0.4995754 980.658435 4.98378048 978.164429 50270.494615 51.2619816
[19,20) 19 0.0050301743 0.0050175442 0.4995808 975.674654 4.89549071 973.224857 49292.330186 50.5212777
[20,21) 20 0.0062491457 0.0062296604 0.4994792 970.779164 6.04762454 967.752202 48319.105329 49.7735295
[21,22) 21 0.0074579158 0.0074301746 0.4993785 964.731539 7.16812376 961.143022 47351.353127 49.0824143
[22,23) 22 0.0059600192 0.0059422935 0.4995033 957.563415 5.69012286 954.715528 46390.210105 48.4460970
[23,24) 23 0.0044413896 0.0044315412 0.4996299 951.873293 4.21826570 949.762599 45435.494577 47.7327129
[24,25) 24 0.0029723742 0.0029679610 0.4997523 947.655027 2.81260320 946.248029 44485.731978 46.9429600
[25,26) 25 0.0033792278 0.0033735247 0.4997184 944.842424 3.18744923 943.247802 43539.483950 46.0812119
[26,27) 26 0.0035336286 0.0035273927 0.4997055 941.654974 3.32158687 939.993203 42596.236148 45.2355027
[27,28) 27 0.0029531225 0.0029487663 0.4997539 938.333388 2.76692589 936.949244 41656.242945 44.3938620
[28,29) 28 0.0024439901 0.0024410060 0.4997963 935.566462 2.28372331 934.424135 40719.293701 43.5236783
[29,30) 29 0.0014422678 0.0014412282 0.4998798 933.282738 1.34507342 932.610040 39784.869566 42.6289568
[30,31) 30 0.0025980260 0.0025946541 0.4997835 931.937665 2.41805588 930.728114 38852.259526 41.6897621
[31,32) 31 0.0023640659 0.0023612737 0.4998030 929.519609 2.19485018 928.421752 37921.531413 40.7969138
[32,33) 32 0.0036836470 0.0036768707 0.4996930 927.324759 3.40965323 925.618886 36993.109661 39.8922916
[33,34) 33 0.0047142261 0.0047031316 0.4996071 923.915106 4.34529435 921.740751 36067.490775 39.0376676
[34,35) 34 0.0017906917 0.0017890894 0.4998508 919.569811 1.64519262 918.746970 35145.750024 38.2197736
[35,36) 35 0.0007949112 0.0007945953 0.4999338 917.924619 0.72937859 917.559881 34227.003054 37.2873789
[36,37) 36 0.0011270474 0.0011264125 0.4999061 917.195240 1.03314017 916.678573 33309.443173 36.3166333
[37,38) 37 0.0020136252 0.0020115992 0.4998322 916.162100 1.84295099 915.240315 32392.764600 35.3570232
[38,39) 38 0.0020461320 0.0020440401 0.4998295 914.319149 1.86890498 913.384378 31477.524285 34.4272832
[39,40) 39 0.0027291673 0.0027254465 0.4997726 912.450244 2.48683437 911.206261 30564.139907 33.4967743
[40,41) 40 0.0037589837 0.0037519276 0.4996868 909.963410 3.41411680 908.255282 29652.933646 32.5869517
[41,42) 41 0.0052799301 0.0052660158 0.4995600 906.549293 4.77390287 904.160241 28744.678364 31.7077941
[42,43) 42 0.0047809169 0.0047695065 0.4996016 901.775390 4.30102357 899.623165 27840.518123 30.8730072
[43,44) 43 0.0054725058 0.0054575589 0.4995440 897.474366 4.89801919 895.023123 26940.894958 30.0185676
[44,45) 44 0.0056351444 0.0056192967 0.4995304 892.576347 5.01565136 890.066166 26045.871835 29.1805535
[45,46) 45 0.0028025849 0.0027986613 0.4997665 887.560696 2.48398175 886.318125 25155.805669 28.3426314
[46,47) 46 0.0036888158 0.0036820205 0.4996926 885.076714 3.25887063 883.446277 24269.487544 27.4207729
[47,48) 47 0.0045321349 0.0045218803 0.4996223 881.817844 3.98747471 879.822600 23386.041267 26.5202632
[48,49) 48 0.0075432547 0.0075148758 0.4993714 877.830369 6.59678620 874.527829 22506.218667 25.6384599
[49,50) 49 0.0090913437 0.0090501424 0.4992424 871.233583 7.88478802 867.285215 21631.690838 24.8288074
[50,51) 50 0.0069545828 0.0069304556 0.4994205 863.348795 5.98340052 860.353627 20764.405623 24.0510044
[51,52) 51 0.0070050262 0.0069805482 0.4994162 857.365394 5.98488045 854.369460 19904.051996 23.2153667
[52,53) 52 0.0059988946 0.0059809371 0.4995001 851.380514 5.09205334 848.831941 19049.682536 22.3750511
[53,54) 53 0.0073160795 0.0072893822 0.4993903 846.288460 6.16892001 843.200239 18200.850595 21.5066747
[54,55) 54 0.0090440534 0.0090032789 0.4992463 840.119540 7.56383056 836.331924 17357.650356 20.6609292
[55,56) 55 0.0081485249 0.0081154157 0.4993210 832.555710 6.75653564 829.172854 16521.318431 19.8440996
[56,57) 56 0.0106842726 0.0106273985 0.4991096 825.799174 8.77609691 821.403312 15692.145577 19.0023750
[57,58) 57 0.0115498437 0.0114834003 0.4990375 817.023077 9.38220302 812.322945 14870.742265 18.2011288
[58,59) 58 0.0110615634 0.0110006092 0.4990782 807.640874 8.88454167 803.190414 14058.419320 17.4067705
[59,60) 59 0.0124208664 0.0123440458 0.4989649 798.756332 9.85988474 793.816184 13255.228906 16.5948342
[60,61) 60 0.0144503689 0.0143464635 0.4987958 788.896448 11.31787406 783.223882 12461.412722 15.7960056
[61,62) 61 0.0182529188 0.0180873433 0.4984789 777.578574 14.06433057 770.525016 11678.188840 15.0186608
[62,63) 62 0.0195952083 0.0194044700 0.4983671 763.514243 14.81558926 756.082256 10907.663825 14.2861301
[63,64) 63 0.0207961093 0.0205813614 0.4982670 748.698654 15.40923757 740.967331 10151.581569 13.5589686
[64,65) 64 0.0210295435 0.0208099646 0.4982476 733.289416 15.25972676 725.632811 9410.614238 12.8334243
[65,66) 65 0.0265175799 0.0261690762 0.4977902 718.029690 18.79017363 708.593081 8684.981427 12.0955743
[66,67) 66 0.0295011240 0.0290702137 0.4975416 699.239516 20.32704214 689.026023 7976.388346 11.4072334
[67,68) 67 0.0304813644 0.0300214920 0.4974599 678.912474 20.38196539 668.669719 7287.362323 10.7338760
[68,69) 68 0.0413323071 0.0404897751 0.4965557 658.530508 26.66375218 645.106795 6618.692604 10.0507000
[69,70) 69 0.0532945865 0.0518993263 0.4955590 631.866756 32.79345898 615.324391 5973.585809 9.4538694
[70,71) 70 0.0593725101 0.0576443332 0.4950526 599.073297 34.53318076 581.635857 5358.261418 8.9442501
[71,72) 71 0.0584736855 0.0567969400 0.4951275 564.540116 32.06415111 548.351807 4776.625561 8.4610915
[72,73) 72 0.0576426993 0.0560128255 0.4951967 532.475965 29.82548334 517.419963 4228.273754 7.9407786
[73,74) 73 0.0704492436 0.0680249580 0.4941297 502.650482 34.19277791 485.353372 3710.853791 7.3825728
[74,75) 74 0.0914065364 0.0873533886 0.4923838 468.457704 40.92136788 447.685357 3225.500419 6.8853610
[75,76) 75 0.0982415067 0.0935700316 0.4918145 427.536336 40.00458849 407.206585 2777.815062 6.4972608
[76,77) 76 0.1033650357 0.0982022750 0.4913878 387.531748 38.05649926 368.175747 2370.608477 6.1171981
[77,78) 77 0.1164517919 0.1099269953 0.4902979 349.475248 38.41676401 329.894142 2002.432730 5.7298270
[78,79) 78 0.1351023744 0.1263735299 0.4887449 311.058484 39.30955867 290.961272 1672.538588 5.3769264
[79,80) 79 0.1314568557 0.1231828960 0.4890484 271.748926 33.47481966 254.644914 1381.577316 5.0840213
[80,81) 80 0.1604955189 0.1482783598 0.4866311 238.274106 35.33089364 220.136325 1126.932402 4.7295630
[81,82) 81 0.1710565181 0.1572260599 0.4857522 202.943212 31.90796167 186.534615 906.796078 4.4682257
[82,83) 82 0.1646344420 0.1517962850 0.4862867 171.035251 25.96251567 157.697960 720.261463 4.2111872
[83,84) 83 0.1875322304 0.1709976013 0.4843815 145.072735 24.80708972 132.281740 562.563503 3.8778031
[84,85) 84 0.2368323279 0.2108764091 0.4802824 120.265645 25.36118744 107.084990 430.281763 3.5777612
[85,86) 85 0.2485966996 0.2201055583 0.4793049 94.904458 20.88899871 84.027659 323.196773 3.4054962
[86,87) 86 0.2297176885 0.2052420598 0.4808737 74.015459 15.19108532 66.129367 239.169114 3.2313400
[87,88) 87 0.2439736928 0.2164917541 0.4796890 58.824374 12.73499190 52.198218 173.039747 2.9416335
[88,89) 88 0.2689273365 0.2358012170 0.4776164 46.089382 10.86793238 40.412152 120.841530 2.6218952
[89,90) 89 0.4303895251 0.3497442455 0.4642444 35.221450 12.31849934 28.621745 80.429378 2.2835340
[90,91) 90 0.3485854399 0.2943143813 0.4710099 22.902950 6.74066765 19.337204 51.807633 2.2620506
[91,92) 91 0.4209692946 0.3435897436 0.4650224 16.162283 5.55319456 13.191448 32.470429 2.0090249
[92,93) 92 0.4970323016 0.3916666667 0.4587502 10.609088 4.15522618 8.360073 19.278981 1.8172138
[93,94) 93 0.3930425881 0.3250000000 0.4673305 6.453862 2.09750513 5.336585 10.918908 1.6918410
[94,95) 94 0.2231435513 0.2000000000 0.4814201 4.356357 0.87127136 3.904533 5.582323 1.2814201
[95,+) 95 0.1266861302 0.0001000000 0.4814201 3.485085 3.48508544 1.677790 1.677790 0.4814201
Tabel 4: Overlevelsestavle. Kvinder
beregnet med MortalityLaws
x.int x mx qx ax lx dx Lx Tx ex
[0,1) 0 0.0045248946 0.0045146727 0.4996229 1000.00000 4.51467269 997.740961 73445.091835 73.4450918
[1,2) 1 0.0018716327 0.0018698823 0.4998440 995.48533 1.86144042 994.554317 72447.350874 72.7759103
[2,3) 2 0.0005269761 0.0005268373 0.4999561 993.62389 0.52347810 993.362125 71452.796557 71.9113112
[3,4) 3 0.0005257816 0.0005256434 0.4999562 993.10041 0.52201666 992.839378 70459.434432 70.9489532
[4,5) 4 0.0002644453 0.0002644104 0.4999780 992.57839 0.26244801 992.447162 69466.595054 69.9860037
[5,6) 5 0.0002763576 0.0002763194 0.4999770 992.31594 0.27419617 992.178840 68474.147892 69.0043814
[6,7) 6 0.0002763576 0.0002763194 0.4999770 992.04175 0.27412041 991.904681 67481.969052 68.0233157
[7,8) 7 0.0001000050 0.0001000000 0.4999917 991.76763 0.09917676 991.718038 66490.064371 67.0419789
[8,9) 8 0.0001000050 0.0001000000 0.4999917 991.66845 0.09916685 991.618867 65498.346333 66.0486338
[9,10) 9 0.0002932121 0.0002931692 0.4999756 991.56928 0.29069753 991.423928 64506.727466 65.0551893
[10,11) 10 0.0002932121 0.0002931692 0.4999756 991.27859 0.29061231 991.133273 63515.303538 64.0741204
[11,12) 11 0.0002905710 0.0002905288 0.4999758 990.98797 0.28791051 990.844012 62524.170265 63.0927639
[12,13) 12 0.0005833533 0.0005831831 0.4999514 990.70006 0.57775957 990.411156 61533.326253 62.1109542
[13,14) 13 0.0005831001 0.0005829301 0.4999514 990.12230 0.57717213 989.833690 60542.915097 61.1469056
[14,15) 14 0.0008788972 0.0008785111 0.4999268 989.54513 0.86932634 989.110405 59553.081407 60.1822792
[15,16) 15 0.0012499547 0.0012491738 0.4998958 988.67581 1.23502794 988.058163 58563.971002 59.2347569
[16,17) 16 0.0009993898 0.0009988905 0.4999167 987.44078 0.98634526 986.947523 57575.912839 58.3082187
[17,18) 17 0.0015374556 0.0015362744 0.4998719 986.45443 1.51546465 985.696506 56588.965316 57.3660206
[18,19) 18 0.0017784391 0.0017768587 0.4998518 984.93897 1.75009733 984.063660 55603.268811 56.4535171
[19,20) 19 0.0017537822 0.0017522452 0.4998539 983.18887 1.72278800 982.327225 54619.205151 55.5531158
[20,21) 20 0.0023450219 0.0023422745 0.4998046 981.46608 2.29886298 980.316202 53636.877926 54.6497519
[21,22) 21 0.0026075062 0.0026041097 0.4997827 979.16722 2.54985881 977.891736 52656.561725 53.7768837
[22,23) 22 0.0020076835 0.0020056695 0.4998327 976.61736 1.95877165 975.637647 51678.669989 52.9159854
[23,24) 23 0.0019687101 0.0019667735 0.4998359 974.65859 1.91693266 973.699808 50703.032342 52.0213262
[24,25) 24 0.0019344235 0.0019325537 0.4998388 972.74166 1.87987551 971.801415 49729.332534 51.1228569
[25,26) 25 0.0008003569 0.0008000367 0.4999333 970.86178 0.77672505 970.473366 48757.531118 50.2208781
[26,27) 26 0.0018173718 0.0018157214 0.4998486 970.08506 1.76140418 969.204087 47787.057752 49.2606885
[27,28) 27 0.0020632716 0.0020611445 0.4998281 968.32365 1.99585499 967.325381 46817.853665 48.3493857
[28,29) 28 0.0012673592 0.0012665564 0.4998944 966.32780 1.22390867 965.715713 45850.528284 47.4482142
[29,30) 29 0.0007572889 0.0007570023 0.4999369 965.10389 0.73058583 964.738549 44884.812571 46.5077523
[30,31) 30 0.0002448880 0.0002448580 0.4999796 964.37330 0.23613450 964.255230 43920.074022 45.5426067
[31,32) 31 0.0004851871 0.0004850694 0.4999596 964.13717 0.46767341 963.903312 42955.818792 44.5536385
[32,33) 32 0.0009821666 0.0009816844 0.4999182 963.66949 0.94601935 963.196407 41991.915480 43.5750179
[33,34) 33 0.0012474289 0.0012466511 0.4998960 962.72347 1.20018031 962.123260 41028.719073 42.6173456
[34,35) 34 0.0010288565 0.0010283274 0.4999143 961.52329 0.98876078 961.028829 40066.595813 41.6699169
[35,36) 35 0.0013518773 0.0013509639 0.4998873 960.53453 1.29764751 959.885564 39105.566984 40.7122968
[36,37) 36 0.0014165341 0.0014155313 0.4998820 959.23689 1.35782983 958.557811 38145.681420 39.7666958
[37,38) 37 0.0008899538 0.0008895580 0.4999258 957.87906 0.85208893 957.452949 37187.123609 38.8223580
[38,39) 38 0.0015391190 0.0015379352 0.4998717 957.02697 1.47184544 956.290856 36229.670661 37.8564783
[39,40) 39 0.0028360261 0.0028320084 0.4997637 955.55512 2.70614013 954.201412 35273.379805 36.9140189
[40,41) 40 0.0022613796 0.0022588246 0.4998116 952.84898 2.15231875 951.772417 34319.178392 36.0174372
[41,42) 41 0.0006631300 0.0006629102 0.4999447 950.69666 0.63022649 950.381515 33367.405976 35.0978469
[42,43) 42 0.0007432182 0.0007429421 0.4999381 950.06644 0.70584431 949.713471 32417.024461 34.1207975
[43,44) 43 0.0023579730 0.0023551952 0.4998035 949.36059 2.23592949 948.242188 31467.310990 33.1457944
[44,45) 44 0.0028881451 0.0028839784 0.4997593 947.12466 2.73148710 945.758262 30519.068802 32.2228636
[45,46) 45 0.0035353035 0.0035290617 0.4997054 944.39318 3.33282174 942.725783 29573.310540 31.3146169
[46,47) 46 0.0031827826 0.0031777229 0.4997348 941.06035 2.99042904 939.564346 28630.584757 30.4237498
[47,48) 47 0.0018424694 0.0018407731 0.4998465 938.06992 1.72677391 937.206273 27691.020410 29.5191432
[48,49) 48 0.0013639901 0.0013630603 0.4998863 936.34315 1.27629219 935.704860 26753.814138 28.5726596
[49,50) 49 0.0012938505 0.0012930139 0.4998922 935.06686 1.20905443 934.462201 25818.109278 27.6109767
[50,51) 50 0.0043012599 0.0042920227 0.4996416 933.85780 4.00813893 931.852298 24883.647077 26.6460771
[51,52) 51 0.0085011580 0.0084651253 0.4992916 929.84967 7.87129396 925.908442 23951.794778 25.7587819
[52,53) 52 0.0059449658 0.0059273294 0.4995046 921.97837 5.46486955 919.243229 23025.886336 24.9744322
[53,54) 53 0.0044319613 0.0044221546 0.4996307 916.51350 4.05296444 914.485523 22106.643107 24.1203682
[54,55) 54 0.0068318583 0.0068085742 0.4994307 912.46054 6.21255526 909.350723 21192.157584 23.2252867
[55,56) 55 0.0071685759 0.0071429429 0.4994026 906.24798 6.47327759 903.007476 20282.806861 22.3810781
[56,57) 56 0.0087498920 0.0087117231 0.4992708 899.77470 7.83858812 895.849695 19379.799384 21.5385021
[57,58) 57 0.0096257628 0.0095795834 0.4991979 891.93612 8.54437640 887.657075 18483.949689 20.7234009
[58,59) 58 0.0075173703 0.0074891855 0.4993736 883.39174 6.61588465 880.079653 17596.292615 19.9190142
[59,60) 59 0.0064275548 0.0064069423 0.4994644 876.77586 5.61745230 873.964120 16716.212961 19.0655489
[60,61) 60 0.0086813898 0.0086438154 0.4992766 871.15840 7.53013240 867.387889 15842.248841 18.1852678
[61,62) 61 0.0107141518 0.0106569597 0.4991072 863.62827 9.20365169 859.018228 14974.860952 17.3394752
[62,63) 62 0.0134310349 0.0133412410 0.4988808 854.42462 11.39908474 848.712318 14115.842724 16.5208755
[63,64) 63 0.0172063970 0.0170592124 0.4985661 843.02553 14.38135162 835.814238 13267.130406 15.7375190
[64,65) 64 0.0158331998 0.0157085136 0.4986806 828.64418 13.01676840 822.118624 12431.316168 15.0019953
[65,66) 65 0.0142630656 0.0141618300 0.4988114 815.62741 11.55077679 809.838297 11609.197544 14.2334568
[66,67) 66 0.0264473730 0.0261007041 0.4977961 804.07664 20.98696636 793.536901 10799.359247 13.4307586
[67,68) 67 0.0323684748 0.0318502224 0.4973027 783.08967 24.94158019 770.551606 10005.822347 12.7773647
[68,69) 68 0.0289023498 0.0284886719 0.4975915 758.14809 21.59863222 747.296755 9235.270741 12.1813546
[69,70) 69 0.0307404152 0.0302727331 0.4974383 736.54946 22.29736521 725.343658 8487.973986 11.5239702
[70,71) 70 0.0362570775 0.0356076620 0.4969786 714.25209 25.43284713 701.458828 7762.630328 10.8681940
[71,72) 71 0.0380823249 0.0373663111 0.4968265 688.81925 25.73863424 675.868249 7061.171500 10.2511240
[72,73) 72 0.0421931769 0.0413154330 0.4964840 663.08061 27.39546261 649.286559 6385.303251 9.6297541
[73,74) 73 0.0541294673 0.0526905471 0.4954894 635.68515 33.49459830 618.786771 5736.016692 9.0233612
[74,75) 74 0.0591934032 0.0574755357 0.4950675 602.19055 34.61122455 584.714219 5117.229922 8.4976922
[75,76) 75 0.0690283646 0.0666997930 0.4942481 567.57933 37.85742358 548.432863 4532.515702 7.9856955
[76,77) 76 0.0761755369 0.0733464696 0.4936527 529.72190 38.85323148 510.048672 3984.082840 7.5210838
[77,78) 77 0.0641867766 0.0621701815 0.4946515 490.86867 30.51739441 475.446751 3474.034167 7.0773190
[78,79) 78 0.0798346977 0.0767310478 0.4933478 460.35128 35.32323585 442.454683 2998.587416 6.5136941
[79,80) 79 0.1206577302 0.1136627258 0.4899476 425.02804 48.30984573 400.387490 2556.132733 6.0140332
[80,81) 80 0.1375832116 0.1285381688 0.4885383 376.71820 48.42266701 351.951858 2155.745243 5.7224346
[81,82) 81 0.1282838843 0.1203963620 0.4893126 328.29553 39.52558732 308.110310 1803.793385 5.4944196
[82,83) 82 0.1375378082 0.1284986006 0.4885421 288.76994 37.10653335 269.791513 1495.683075 5.1794971
[83,84) 83 0.1374284504 0.1284032898 0.4885512 251.66341 32.31440952 235.136243 1225.891563 4.8711554
[84,85) 84 0.1310204057 0.1228001257 0.4890848 219.34900 26.93608459 205.586942 990.755319 4.5167989
[85,86) 85 0.1774640415 0.1626088899 0.4852191 192.41291 31.28805033 176.306423 785.168377 4.0806428
[86,87) 86 0.2011629171 0.1822208095 0.4832477 161.12486 29.36030307 145.952860 608.861954 3.7788206
[87,88) 87 0.1743857321 0.1600271693 0.4854752 131.76456 21.08590963 120.915337 462.909094 3.5131533
[88,89) 88 0.2193199623 0.1969352734 0.4817380 110.67865 21.79653037 99.382337 341.993757 3.0899704
[89,90) 89 0.3071210829 0.2644384684 0.4744467 88.88212 23.50385182 76.529594 242.611420 2.7295863
[90,91) 90 0.2992560561 0.2586304471 0.4750991 65.37827 16.90881086 56.502819 166.081826 2.5403216
[91,92) 91 0.3209349654 0.2745295699 0.4733012 48.46946 13.30629941 41.461046 109.579007 2.2607847
[92,93) 92 0.4404448270 0.3562500000 0.4634144 35.16316 12.52687519 28.441417 68.117961 1.9371969
[93,94) 93 0.3242396682 0.2769230769 0.4730273 22.63628 6.26850920 19.332950 39.676543 1.7527852
[94,95) 94 0.2676413216 0.2348178138 0.4777231 16.36777 3.84344491 14.360432 20.343593 1.2429053
[95,+) 95 0.2209226200 0.4245614035 0.4777231 12.52433 12.52432912 5.983162 5.983162 0.4777231




Dødelighed

Dødelighed efter køn, : mænd, født i Grønland

Tabel 5: Dødshyppigheder beregnet som a- og b-grupper.
2020 - 2024
Født i Grønland Total
age A B A B
Kvinder Mænd I alt Kvinder Mænd I alt Kvinder Mænd I alt Kvinder Mænd I alt
0 0.0005 0.0005 0.0005 0.0045 0.0033 0.0039 0.0005 0.0005 0.0005 0.0041 0.0030 0.0036
1 0.0026 0.0031 0.0029 0.0019 0.0018 0.0019 0.0025 0.0030 0.0027 0.0018 0.0018 0.0018
2 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005
3 0.0011 0.0001 0.0005 0.0005 0.0003 0.0004 0.0010 0.0001 0.0005 0.0005 0.0002 0.0004
4 0.0001 0.0001 0.0001 0.0003 0.0001 0.0001 0.0001 0.0001 0.0001 0.0002 0.0001 0.0001
5 0.0006 0.0005 0.0005 0.0003 0.0005 0.0004 0.0005 0.0005 0.0005 0.0003 0.0005 0.0004
6 0.0001 0.0005 0.0003 0.0003 0.0008 0.0005 0.0001 0.0005 0.0003 0.0003 0.0007 0.0005
7 0.0001 0.0005 0.0003 0.0001 0.0003 0.0001 0.0001 0.0005 0.0003 0.0001 0.0002 0.0001
8 0.0001 0.0005 0.0003 0.0001 0.0003 0.0001 0.0001 0.0005 0.0003 0.0001 0.0003 0.0001
9 0.0006 0.0001 0.0003 0.0003 0.0003 0.0003 0.0006 0.0001 0.0003 0.0003 0.0003 0.0003
10 0.0001 0.0001 0.0001 0.0003 0.0001 0.0001 0.0001 0.0001 0.0001 0.0003 0.0001 0.0001
11 0.0001 0.0001 0.0001 0.0003 0.0001 0.0001 0.0001 0.0001 0.0001 0.0003 0.0001 0.0001
12 0.0006 0.0001 0.0003 0.0006 0.0001 0.0003 0.0005 0.0001 0.0003 0.0006 0.0001 0.0003
13 0.0006 0.0011 0.0008 0.0006 0.0005 0.0006 0.0006 0.0010 0.0008 0.0006 0.0005 0.0005
14 0.0012 0.0011 0.0011 0.0009 0.0011 0.0010 0.0011 0.0011 0.0011 0.0008 0.0010 0.0009
15 0.0013 0.0018 0.0016 0.0012 0.0021 0.0017 0.0013 0.0018 0.0015 0.0012 0.0020 0.0016
16 0.0013 0.0038 0.0026 0.0010 0.0034 0.0023 0.0013 0.0036 0.0025 0.0010 0.0033 0.0022
17 0.0018 0.0040 0.0029 0.0015 0.0039 0.0028 0.0017 0.0038 0.0028 0.0015 0.0037 0.0026
18 0.0006 0.0050 0.0028 0.0018 0.0051 0.0035 0.0011 0.0048 0.0030 0.0020 0.0048 0.0034
19 0.0029 0.0039 0.0034 0.0018 0.0050 0.0034 0.0027 0.0037 0.0032 0.0019 0.0048 0.0034
20 0.0023 0.0062 0.0043 0.0023 0.0062 0.0043 0.0022 0.0059 0.0040 0.0022 0.0059 0.0041
21 0.0023 0.0067 0.0045 0.0026 0.0074 0.0050 0.0021 0.0063 0.0042 0.0024 0.0070 0.0047
22 0.0017 0.0066 0.0042 0.0020 0.0059 0.0040 0.0016 0.0061 0.0039 0.0019 0.0055 0.0037
23 0.0027 0.0048 0.0038 0.0020 0.0044 0.0032 0.0025 0.0044 0.0035 0.0018 0.0041 0.0030
24 0.0016 0.0036 0.0026 0.0019 0.0030 0.0025 0.0015 0.0033 0.0024 0.0018 0.0027 0.0023
25 0.0001 0.0010 0.0005 0.0008 0.0034 0.0021 0.0001 0.0009 0.0005 0.0007 0.0030 0.0019
26 0.0015 0.0049 0.0033 0.0018 0.0035 0.0027 0.0014 0.0043 0.0029 0.0016 0.0031 0.0024
27 0.0025 0.0029 0.0027 0.0021 0.0029 0.0025 0.0022 0.0025 0.0024 0.0018 0.0026 0.0022
28 0.0010 0.0019 0.0015 0.0013 0.0024 0.0019 0.0009 0.0016 0.0013 0.0011 0.0021 0.0016
29 0.0010 0.0009 0.0010 0.0008 0.0014 0.0011 0.0008 0.0008 0.0008 0.0006 0.0012 0.0009
30 0.0005 0.0024 0.0014 0.0002 0.0026 0.0014 0.0004 0.0020 0.0012 0.0002 0.0024 0.0013
31 0.0001 0.0034 0.0017 0.0005 0.0024 0.0014 0.0001 0.0032 0.0017 0.0004 0.0022 0.0013
32 0.0010 0.0020 0.0015 0.0010 0.0037 0.0023 0.0009 0.0021 0.0015 0.0009 0.0033 0.0021
33 0.0015 0.0067 0.0041 0.0012 0.0047 0.0030 0.0013 0.0055 0.0035 0.0011 0.0041 0.0026
34 0.0011 0.0021 0.0016 0.0010 0.0018 0.0014 0.0014 0.0018 0.0016 0.0011 0.0017 0.0014
35 0.0011 0.0006 0.0008 0.0014 0.0008 0.0011 0.0010 0.0009 0.0010 0.0014 0.0009 0.0011
36 0.0018 0.0011 0.0015 0.0014 0.0011 0.0013 0.0016 0.0009 0.0012 0.0012 0.0009 0.0011
37 0.0012 0.0023 0.0018 0.0009 0.0020 0.0015 0.0011 0.0019 0.0015 0.0008 0.0016 0.0012
38 0.0013 0.0012 0.0012 0.0015 0.0020 0.0018 0.0011 0.0010 0.0010 0.0013 0.0017 0.0015
39 0.0032 0.0031 0.0032 0.0028 0.0027 0.0028 0.0028 0.0025 0.0027 0.0025 0.0022 0.0023
40 0.0027 0.0052 0.0040 0.0023 0.0038 0.0030 0.0023 0.0041 0.0033 0.0020 0.0030 0.0025
41 0.0001 0.0021 0.0011 0.0007 0.0053 0.0030 0.0006 0.0016 0.0011 0.0009 0.0042 0.0026
42 0.0008 0.0072 0.0041 0.0007 0.0048 0.0028 0.0007 0.0061 0.0036 0.0013 0.0043 0.0029
43 0.0016 0.0038 0.0028 0.0024 0.0055 0.0040 0.0021 0.0035 0.0028 0.0023 0.0047 0.0036
44 0.0026 0.0080 0.0054 0.0029 0.0056 0.0043 0.0022 0.0061 0.0043 0.0024 0.0046 0.0036
45 0.0054 0.0016 0.0034 0.0035 0.0028 0.0031 0.0046 0.0018 0.0031 0.0030 0.0027 0.0028
46 0.0027 0.0057 0.0043 0.0032 0.0037 0.0034 0.0031 0.0049 0.0041 0.0031 0.0031 0.0031
47 0.0018 0.0041 0.0030 0.0018 0.0045 0.0033 0.0016 0.0031 0.0024 0.0020 0.0037 0.0029
48 0.0017 0.0073 0.0046 0.0014 0.0075 0.0045 0.0015 0.0073 0.0047 0.0012 0.0065 0.0042
49 0.0016 0.0084 0.0051 0.0013 0.0091 0.0053 0.0014 0.0063 0.0042 0.0011 0.0074 0.0046
50 0.0043 0.0076 0.0060 0.0043 0.0069 0.0057 0.0039 0.0059 0.0050 0.0038 0.0052 0.0046
51 0.0044 0.0043 0.0044 0.0085 0.0070 0.0077 0.0040 0.0034 0.0037 0.0076 0.0054 0.0064
52 0.0074 0.0044 0.0058 0.0059 0.0060 0.0060 0.0068 0.0040 0.0053 0.0057 0.0052 0.0054
53 0.0036 0.0094 0.0066 0.0044 0.0073 0.0059 0.0038 0.0086 0.0064 0.0044 0.0071 0.0058
54 0.0083 0.0060 0.0071 0.0068 0.0090 0.0079 0.0077 0.0066 0.0071 0.0063 0.0087 0.0076
55 0.0048 0.0063 0.0056 0.0071 0.0081 0.0076 0.0049 0.0057 0.0053 0.0069 0.0078 0.0074
56 0.0102 0.0142 0.0123 0.0087 0.0106 0.0097 0.0095 0.0131 0.0115 0.0083 0.0097 0.0091
57 0.0084 0.0100 0.0092 0.0096 0.0115 0.0106 0.0079 0.0087 0.0084 0.0089 0.0101 0.0095
58 0.0102 0.0122 0.0112 0.0075 0.0110 0.0093 0.0096 0.0102 0.0099 0.0070 0.0092 0.0082
59 0.0043 0.0132 0.0088 0.0064 0.0123 0.0094 0.0040 0.0127 0.0087 0.0060 0.0114 0.0089
60 0.0085 0.0131 0.0109 0.0086 0.0143 0.0116 0.0081 0.0119 0.0102 0.0082 0.0137 0.0112
61 0.0116 0.0203 0.0162 0.0107 0.0181 0.0145 0.0115 0.0180 0.0151 0.0104 0.0161 0.0135
62 0.0161 0.0185 0.0174 0.0133 0.0194 0.0165 0.0150 0.0160 0.0156 0.0128 0.0166 0.0149
63 0.0156 0.0200 0.0180 0.0171 0.0206 0.0190 0.0145 0.0183 0.0167 0.0159 0.0186 0.0175
64 0.0180 0.0247 0.0216 0.0157 0.0208 0.0185 0.0167 0.0230 0.0203 0.0146 0.0197 0.0175
65 0.0160 0.0291 0.0231 0.0142 0.0262 0.0207 0.0158 0.0258 0.0214 0.0136 0.0239 0.0194
66 0.0278 0.0300 0.0290 0.0261 0.0291 0.0277 0.0263 0.0268 0.0265 0.0249 0.0258 0.0254
67 0.0290 0.0360 0.0328 0.0319 0.0300 0.0309 0.0276 0.0322 0.0302 0.0301 0.0266 0.0282
68 0.0368 0.0404 0.0387 0.0285 0.0405 0.0350 0.0352 0.0363 0.0359 0.0272 0.0368 0.0326
69 0.0294 0.0559 0.0433 0.0303 0.0519 0.0417 0.0283 0.0507 0.0407 0.0290 0.0474 0.0392
70 0.0442 0.0677 0.0565 0.0356 0.0576 0.0471 0.0427 0.0617 0.0533 0.0343 0.0531 0.0446
71 0.0394 0.0617 0.0512 0.0374 0.0568 0.0476 0.0383 0.0577 0.0493 0.0362 0.0527 0.0454
72 0.0529 0.0597 0.0564 0.0413 0.0560 0.0491 0.0515 0.0506 0.0510 0.0402 0.0498 0.0457
73 0.0437 0.0734 0.0593 0.0527 0.0680 0.0607 0.0428 0.0670 0.0566 0.0514 0.0609 0.0568
74 0.0589 0.0893 0.0748 0.0575 0.0874 0.0731 0.0600 0.0800 0.0715 0.0574 0.0802 0.0704
75 0.0751 0.0922 0.0837 0.0667 0.0936 0.0807 0.0738 0.0826 0.0786 0.0667 0.0882 0.0790
76 0.0707 0.1040 0.0866 0.0733 0.0982 0.0856 0.0716 0.1040 0.0889 0.0732 0.0912 0.0831
77 0.0600 0.1140 0.0850 0.0622 0.1099 0.0847 0.0614 0.0967 0.0797 0.0634 0.0974 0.0812
78 0.0764 0.1461 0.1071 0.0767 0.1264 0.0995 0.0781 0.1282 0.1029 0.0781 0.1113 0.0953
79 0.1308 0.1362 0.1331 0.1137 0.1232 0.1181 0.1291 0.1206 0.1250 0.1137 0.1080 0.1111
80 0.1371 0.1679 0.1501 0.1285 0.1483 0.1368 0.1352 0.1631 0.1484 0.1267 0.1437 0.1346
81 0.1423 0.2012 0.1667 0.1204 0.1572 0.1359 0.1408 0.2034 0.1696 0.1187 0.1551 0.1359
82 0.1546 0.1577 0.1559 0.1285 0.1518 0.1381 0.1527 0.1337 0.1440 0.1272 0.1359 0.1312
83 0.1121 0.2262 0.1571 0.1284 0.1710 0.1456 0.1101 0.2379 0.1661 0.1267 0.1763 0.1489
84 0.1375 0.1954 0.1591 0.1228 0.2109 0.1561 0.1414 0.2178 0.1723 0.1241 0.2222 0.1656
85 0.2008 0.2837 0.2316 0.1626 0.2201 0.1837 0.1959 0.3000 0.2370 0.1625 0.2169 0.1845
86 0.1818 0.1786 0.1806 0.1822 0.2052 0.1908 0.1771 0.1562 0.1688 0.1773 0.2004 0.1865
87 0.1772 0.2759 0.2122 0.1600 0.2165 0.1808 0.1739 0.2772 0.2137 0.1555 0.2061 0.1754
88 0.2113 0.3582 0.2584 0.1969 0.2358 0.2109 0.2238 0.3333 0.2624 0.2014 0.2235 0.2102
89 0.2051 0.2857 0.2289 0.2644 0.3497 0.2893 0.2051 0.2909 0.2326 0.2708 0.3542 0.2965
90 0.4045 0.3000 0.3721 0.2586 0.2943 0.2686 0.4045 0.3111 0.3731 0.2586 0.3005 0.2716
91 0.3607 0.4286 0.3820 0.2745 0.3436 0.2975 0.3607 0.3529 0.3579 0.2745 0.3269 0.2949
92 0.4211 0.6667 0.4906 0.3563 0.3917 0.3701 0.4211 0.5714 0.4746 0.3563 0.3481 0.3532
93 0.4000 0.1818 0.3333 0.2769 0.3250 0.2897 0.4000 0.1250 0.2927 0.2769 0.2909 0.2832
94 0.4000 0.5714 0.4444 0.2348 0.2000 0.2146 0.4000 0.8000 0.5333 0.2348 0.2769 0.2470

Udvandringer

Udvandringshyppigheder efter køn, : mænd, født i Grønland

Tabel 6: Udvandringshyppigheder beregnet som a- og b-grupper.
2020 - 2024
I alt
alder Kvinder Mænd I alt
A B A B A B
0 0.0134 0.0970 0.0157 0.0958 0.0145 0.0964
1 0.0321 0.0314 0.0363 0.0335 0.0342 0.0325
2 0.0312 0.0275 0.0286 0.0314 0.0299 0.0294
3 0.0271 0.0279 0.0291 0.0268 0.0281 0.0273
4 0.0271 0.0287 0.0225 0.0224 0.0247 0.0255
5 0.0246 0.0249 0.0148 0.0232 0.0195 0.0240
6 0.0236 0.0241 0.0297 0.0221 0.0268 0.0230
7 0.0230 0.0220 0.0220 0.0220 0.0225 0.0220
8 0.0253 0.0227 0.0244 0.0233 0.0249 0.0231
9 0.0193 0.0229 0.0193 0.0199 0.0193 0.0213
10 0.0203 0.0204 0.0203 0.0199 0.0203 0.0201
11 0.0258 0.0212 0.0198 0.0206 0.0227 0.0209
12 0.0247 0.0234 0.0157 0.0170 0.0201 0.0201
13 0.0276 0.0271 0.0211 0.0189 0.0242 0.0228
14 0.0382 0.0373 0.0311 0.0309 0.0345 0.0340
15 0.1827 0.1642 0.1678 0.1428 0.1750 0.1531
16 0.1653 0.1836 0.1349 0.1574 0.1497 0.1700
17 0.0519 0.0599 0.0408 0.0476 0.0463 0.0536
18 0.0347 0.0396 0.0382 0.0322 0.0365 0.0358
19 0.0573 0.0556 0.0373 0.0403 0.0472 0.0478
20 0.0900 0.0842 0.0585 0.0561 0.0741 0.0699
21 0.0760 0.0866 0.0685 0.0758 0.0722 0.0812
22 0.0641 0.0677 0.0750 0.0716 0.0696 0.0697
23 0.0611 0.0584 0.0513 0.0613 0.0561 0.0598
24 0.0577 0.0606 0.0709 0.0657 0.0644 0.0631
25 0.0598 0.0663 0.0644 0.0664 0.0621 0.0663
26 0.0738 0.0682 0.0602 0.0638 0.0668 0.0660
27 0.0597 0.0657 0.0726 0.0673 0.0663 0.0665
28 0.0695 0.0649 0.0549 0.0652 0.0621 0.0650
29 0.0668 0.0682 0.0634 0.0598 0.0651 0.0639
30 0.0656 0.0694 0.0540 0.0591 0.0597 0.0641
31 0.0660 0.0644 0.0632 0.0617 0.0646 0.0630
32 0.0530 0.0498 0.0593 0.0566 0.0562 0.0533
33 0.0379 0.0424 0.0501 0.0487 0.0442 0.0456
34 0.0522 0.0466 0.0550 0.0472 0.0537 0.0469
35 0.0484 0.0441 0.0423 0.0415 0.0453 0.0428
36 0.0337 0.0353 0.0390 0.0470 0.0365 0.0414
37 0.0330 0.0338 0.0498 0.0476 0.0419 0.0410
38 0.0406 0.0347 0.0456 0.0395 0.0432 0.0372
39 0.0322 0.0329 0.0405 0.0412 0.0366 0.0373
40 0.0293 0.0313 0.0419 0.0409 0.0360 0.0364
41 0.0358 0.0317 0.0428 0.0408 0.0396 0.0366
42 0.0282 0.0273 0.0421 0.0426 0.0357 0.0355
43 0.0250 0.0227 0.0475 0.0449 0.0373 0.0347
44 0.0308 0.0266 0.0528 0.0457 0.0428 0.0371
45 0.0260 0.0314 0.0399 0.0439 0.0337 0.0382
46 0.0406 0.0351 0.0420 0.0411 0.0414 0.0384
47 0.0305 0.0318 0.0380 0.0412 0.0346 0.0371
48 0.0281 0.0274 0.0432 0.0399 0.0365 0.0344
49 0.0286 0.0320 0.0375 0.0383 0.0335 0.0355
50 0.0276 0.0294 0.0414 0.0431 0.0351 0.0369
51 0.0202 0.0232 0.0360 0.0413 0.0287 0.0331
52 0.0250 0.0260 0.0350 0.0368 0.0304 0.0318
53 0.0236 0.0289 0.0271 0.0344 0.0255 0.0319
54 0.0287 0.0277 0.0343 0.0342 0.0317 0.0312
55 0.0267 0.0270 0.0348 0.0345 0.0311 0.0311
56 0.0281 0.0290 0.0289 0.0296 0.0286 0.0293
57 0.0298 0.0293 0.0282 0.0295 0.0289 0.0294
58 0.0258 0.0279 0.0311 0.0290 0.0287 0.0285
59 0.0256 0.0255 0.0241 0.0264 0.0248 0.0260
60 0.0274 0.0236 0.0344 0.0300 0.0312 0.0271
61 0.0247 0.0239 0.0295 0.0347 0.0274 0.0298
62 0.0287 0.0256 0.0428 0.0349 0.0366 0.0307
63 0.0317 0.0280 0.0329 0.0321 0.0324 0.0303
64 0.0288 0.0291 0.0380 0.0343 0.0340 0.0321
65 0.0300 0.0277 0.0348 0.0330 0.0327 0.0307
66 0.0450 0.0374 0.0347 0.0309 0.0392 0.0337
67 0.0298 0.0323 0.0306 0.0326 0.0302 0.0325
68 0.0182 0.0168 0.0310 0.0276 0.0254 0.0229
69 0.0234 0.0168 0.0259 0.0238 0.0248 0.0207
70 0.0114 0.0157 0.0202 0.0237 0.0163 0.0201
71 0.0128 0.0122 0.0209 0.0159 0.0173 0.0142
72 0.0120 0.0081 0.0160 0.0122 0.0143 0.0104
73 0.0058 0.0089 0.0116 0.0094 0.0092 0.0092
74 0.0107 0.0099 0.0064 0.0112 0.0082 0.0107
75 0.0108 0.0078 0.0158 0.0115 0.0136 0.0099
76 0.0067 0.0056 0.0098 0.0076 0.0084 0.0066
77 0.0001 0.0034 0.0088 0.0092 0.0046 0.0065
78 0.0025 0.0025 0.0205 0.0145 0.0114 0.0086
79 0.0057 0.0027 0.0155 0.0202 0.0104 0.0114
80 0.0001 0.0014 0.0194 0.0127 0.0092 0.0068
81 0.0124 0.0061 0.0001 0.0038 0.0067 0.0051
82 0.0001 0.0085 0.0058 0.0028 0.0027 0.0059
83 0.0058 0.0024 0.0001 0.0028 0.0033 0.0026
84 0.0001 0.0001 0.0001 0.0049 0.0001 0.0020
85 0.0001 0.0001 0.0125 0.0049 0.0049 0.0020
86 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
87 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
88 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
89 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
90 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
91 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
92 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
93 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
94 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

Statistikbank tabeller

Endelig skal tabellerne suppleres med metadata for at kunne præsenteres i Statistikbanken.

Dette gøres med Grønlands Statistiks r-pakke ‘pxmake’. 


# source("pxmake_all.R")

# Convert the dataframe px_data_lt a px-file, ready to use
lastupdated    <- "20250331 09:00" # release date
nextupdate     <- "20260308 09:00" # next release date

px_data_lt <- px_data_lt %>% 
  arrange(time) %>% 
  mutate(time=as.character(time))

# get metadata into px-object (z), from previous px-file: BEXLTALL.px
z <- px(file.path(previousPath,"BEXLTALL.px")) %>%
  px_data(px_data_lt) %>% 
  # + update date for : last updated and next update
  px_last_updated(lastupdated) %>%
  px_next_update(nextupdate) %>% 
  # + update description and title
  px_description(data.frame(language = c("en", "da", "kl"),
          value = c(paste0("Life Tables ",(CONST_start_year+5)," - ",
          CONST_end_event_year," <em>[BEELTALL]</em>"),
          paste0("Overlevelsestavler ",(CONST_start_year+5),
          " - ",CONST_end_event_year," <em>[BEDLTALL]</em>"),
          paste0((CONST_start_year+5)," - ",CONST_end_event_year,
          ", Agguaqatigiissillugu ukioqqortussuseq <em>[BENLTALL]</em>")))) %>% 
  px_title(data.frame(language = c("en", "da", "kl"),
                      value = c("Life Tables ",
                        "Overlevelsestavler ",
                        "Agguaqatigiissillugu ukioqqortussuseq")))
# %>% 
#   px_infofile(tribble(~language, ~value,
#                     'en', 'metode til beregning af dødelighed',
#                     'da', 'metode til beregning af dødelighed',
#                     'kl', 'metode til beregning af dødelighed'))

# save px-object to file, overwriting if it already exists
px_save(z, file.path(resultsPath,"BEXLTALL.px"))

#px_save(z, file.path(resultsPath,"BEXLTALL.xlsx"))

source("pxmake_reg.R")

source("pxmake_out.R")