Description
Under-Five Child Mortality Estimation.
Description
Contains functions for calculating under-five child mortality estimates using the Trussell version of the Brass method (United Nations (1990) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf> and United Nations (1983) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf>) as well as applying the cohort-derived methods by Rajaratnam and colleagues (Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) "Measuring Under-Five Mortality: Validation of New Low-Cost Methods" <doi:10.1371/journal.pmed.1000253>).
README.md
Under-Five Child Mortality Estimation using the R Package u5mr
u5mr is a open-source R package for estimating the child mortality. Current implementation includes the Trussell version of the Brass method using the Coale-Demeny model life tables and supporting datasets of coefficients and automatic interpolating values between probabilities of dying at a certain age and model tables.
Installation
To download the developmental version of the u5mr package, use the code below.
# install.packages("devtools")
devtools::install_github("myominnoo/u5mr")
Usage
The first example is using Bangladesh survey data and model South of the Coale-Demeny life table.
library(u5mr)
## Using Bangladesh survey data to estimate child mortality
data("bangladesh")
bang_both <- u5mr_trussell(bangladesh, sex = "both", model = "south", svy_year = 1974.3)
bang_both
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 3014706 1160919 215365 0.3850853 0.1855125 0.9170244 0.1701195
#> 2 20-24 2653155 4901382 997384 1.8473787 0.2034904 0.9954643 0.2025674
#> 3 25-29 2607009 9085852 1937955 3.4851633 0.2132937 0.9906944 0.2113089
#> 4 30-34 2015663 9910256 2261196 4.9166235 0.2281673 1.0075009 0.2298787
#> 5 35-39 1771680 10384001 2490168 5.8611041 0.2398081 1.0293505 0.2468466
#> 6 40-44 1479575 9164329 2415023 6.1938928 0.2635243 1.0094877 0.2660245
#> 7 45-49 1135129 6905673 1959544 6.0836020 0.2837586 0.9972749 0.2829853
#> ti year h q5
#> 1 1.179512 1973.1 0.06844071 0.2936809
#> 2 2.573340 1971.7 0.69835995 0.2393985
#> 3 4.591300 1969.7 0.25513876 0.2298161
#> 4 6.972056 1967.3 0.25803543 0.2298787
#> 5 9.601908 1964.7 0.22544741 0.2291742
#> 6 12.426746 1961.9 0.60317713 0.2373407
#> 7 15.521768 1958.8 0.68837645 0.2391827
bang_male <- u5mr_trussell(bangladesh, child_born = "male_born",
child_dead = "male_dead", sex = "male",
model = "south", svy_year = 1974.3)
bang_male
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 3014706 597248 117165 0.1981115 0.1961748 0.9105728 0.1786314
#> 2 20-24 2653155 2507018 529877 0.9449195 0.2113575 0.9956980 0.2104482
#> 3 25-29 2607009 4675978 1047294 1.7936179 0.2239733 0.9923244 0.2222541
#> 4 30-34 2015663 5109487 1204582 2.5348915 0.2357540 1.0092759 0.2379408
#> 5 35-39 1771680 5435726 1333957 3.0681195 0.2454055 1.0311249 0.2530437
#> 6 40-44 1479575 4883599 1291745 3.3006769 0.2645068 1.0111377 0.2674528
#> 7 45-49 1135129 3714957 1030737 3.2727179 0.2774560 0.9987376 0.2771057
#> ti year h q5
#> 1 1.192502 1973.1 0.08877256 0.3006920
#> 2 2.579408 1971.7 0.13536167 0.2555678
#> 3 4.579127 1969.7 0.47824926 0.2412141
#> 4 6.935584 1967.4 0.32705901 0.2379408
#> 5 9.538031 1964.8 0.21974177 0.2356174
#> 6 12.341232 1962.0 0.42457992 0.2400522
#> 7 15.432413 1958.9 0.24483379 0.2361607
bang_female <- u5mr_trussell(bangladesh, child_born = "female_born",
child_dead = "female_dead", sex = "female",
model = "south", svy_year = 1974.3)
bang_female
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 3014706 563671 98200 0.1869738 0.1742151 0.9238170 0.1609429
#> 2 20-24 2653155 2394364 467507 0.9024591 0.1952531 0.9952065 0.1943172
#> 3 25-29 2607009 4409874 890661 1.6915454 0.2019697 0.9889674 0.1997415
#> 4 30-34 2015663 4800769 1056614 2.3817320 0.2200927 1.0056231 0.2213302
#> 5 35-39 1771680 4948275 1156211 2.7929846 0.2336594 1.0274741 0.2400790
#> 6 40-44 1479575 4280730 1123278 2.8932160 0.2624034 1.0077431 0.2644352
#> 7 45-49 1135129 3190716 928807 2.8108840 0.2910967 0.9957282 0.2898532
#> ti year h q5
#> 1 1.165826 1973.1 0.02505353 0.2857318
#> 2 2.566995 1971.7 0.02066463 0.2394660
#> 3 4.604250 1969.7 0.01367451 0.2177052
#> 4 7.010693 1967.3 0.18157708 0.2213302
#> 5 9.669503 1964.6 0.22027538 0.2221657
#> 6 12.517197 1961.8 0.78201764 0.2342938
#> 7 15.616267 1958.7 0.15543868 0.2425051
## plotting all data points
with(bang_both,
plot(year, q5, type = "b", pch = 19,
ylim = c(0, .45),
col = "black", xlab = "Reference date", ylab = "u5MR",
main = paste0("Under-Five mortality, q(5) in Bangladesh, estimated\n",
"using model South and the Trussell version of the Brass method")))
with(bang_both, text(year, q5, agegrp, cex=0.65, pos=3,col="purple"))
with(bang_male, lines(year, q5, pch = 18, col = "red", type = "b", lty = 2))
with(bang_female,
lines(year, q5, pch = 18, col = "blue", type = "b", lty = 3))
legend("bottomright", legend=c("Both sexes", "Male", "Female"),
col = c("Black", "red", "blue"), lty = 1:3, cex=0.8)
Below, the second example is demonstrated using Panama survey data and model West.
## Using panama survey data to estimate child mortality
data("panama")
pnm_both <- u5mr_trussell(panama, sex = "both", model = "west", svy_year = 1976.5)
pnm_both
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 2695 557 40 0.206679 0.07181329 1.0664294 0.07658380
#> 2 20-24 2095 2633 130 1.256802 0.04937334 1.0404542 0.05137070
#> 3 25-29 1828 4757 312 2.602298 0.06558756 0.9937778 0.06517946
#> 4 30-34 1605 6085 435 3.791277 0.07148726 1.0041775 0.07178590
#> 5 35-39 1362 6722 636 4.935389 0.09461470 1.0221369 0.09670918
#> 6 40-44 1128 6367 686 5.644504 0.10774305 1.0099799 0.10881831
#> 7 45-49 930 5276 689 5.673118 0.13059136 1.0021669 0.13087434
#> ti year h q5
#> 1 1.048001 1975.5 0.81948565 0.10681851
#> 2 2.364852 1974.1 0.92645560 0.05824125
#> 3 4.314966 1972.2 0.67558234 0.07022363
#> 4 6.636520 1969.9 0.77255738 0.07178590
#> 5 9.194406 1967.3 0.77689110 0.08856392
#> 6 11.924192 1964.6 0.07458829 0.09364872
#> 7 14.855814 1961.6 0.62026040 0.10329620
pnm_male <- u5mr_trussell(panama, child_born = "male_born",
child_dead = "male_dead", sex = "male",
model = "west", svy_year = 1976.5)
pnm_male
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 2695 278 24 0.1031540 0.08633094 1.1028540 0.09521042
#> 2 20-24 2095 1380 77 0.6587112 0.05579710 1.0394544 0.05799854
#> 3 25-29 1828 2395 172 1.3101751 0.07181628 0.9850075 0.07073958
#> 4 30-34 1605 3097 236 1.9295950 0.07620278 0.9938784 0.07573630
#> 5 35-39 1362 3444 348 2.5286344 0.10104530 1.0109014 0.10214683
#> 6 40-44 1128 3274 394 2.9024823 0.12034209 0.9983923 0.12014862
#> 7 45-49 930 2682 354 2.8838710 0.13199105 0.9908787 0.13078712
#> ti year h q5
#> 1 0.9648135 1975.5 0.7281892 0.13193006
#> 2 2.3270195 1974.2 0.9840888 0.06559956
#> 3 4.3919097 1972.1 0.6078929 0.07600789
#> 4 6.8621158 1969.6 0.5916396 0.07573630
#> 5 9.5808416 1966.9 0.6441241 0.09378708
#> 6 12.4282225 1964.1 0.2231215 0.10404412
#> 7 15.3637414 1961.1 0.2131179 0.10386235
pnm_female <- u5mr_trussell(panama, child_born = "female_born",
child_dead = "female_dead", sex = "female",
model = "west", svy_year = 1976.5)
pnm_female
#> agegrp women child_born child_dead pi di ki qx
#> 1 15-19 2695 279 16 0.1035250 0.05734767 1.027639 0.05893272
#> 2 20-24 2095 1253 53 0.5980907 0.04229848 1.041099 0.04403690
#> 3 25-29 1828 2362 140 1.2921225 0.05927180 1.002714 0.05943267
#> 4 30-34 1605 2988 199 1.8616822 0.06659973 1.014781 0.06758414
#> 5 35-39 1362 3278 288 2.4067548 0.08785845 1.033741 0.09082286
#> 6 40-44 1128 3093 292 2.7420213 0.09440672 1.021957 0.09647958
#> 7 45-49 930 2594 335 2.7892473 0.12914418 1.013832 0.13093046
#> ti year h q5
#> 1 1.136167 1975.4 0.89036801 0.08250300
#> 2 2.407031 1974.1 0.82492745 0.05003973
#> 3 4.238697 1972.3 0.75975955 0.06427108
#> 4 6.406109 1970.1 0.97378159 0.06758414
#> 5 8.796807 1967.7 0.91344410 0.08287914
#> 6 11.404035 1965.1 0.88800323 0.08246445
#> 7 14.331051 1962.2 0.04466646 0.10226980
## plotting all data points
with(pnm_both,
plot(year, q5, type = "b", pch = 19,
ylim = c(0, .2), col = "black", xlab = "Reference date", ylab = "u5MR",
main = paste0("Under-Five mortality, q(5) in Panama, estimated\n",
"using model West and the Trussell version of the Brass method")))
with(pnm_both, text(year, q5, agegrp, cex=0.65, pos=3,col="purple"))
with(pnm_male,
lines(year, q5, pch = 18, col = "red", type = "b", lty = 2))
with(pnm_female,
lines(year, q5, pch = 18, col = "blue", type = "b", lty = 3))
legend("bottomleft", legend=c("Both sexes", "Male", "Female"),
col = c("Black", "red", "blue"), lty = 1:3, cex=0.8)
Bug Reports / Change Requests
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.
Getting Help
For questions and other discussion, please directly email me [email protected].
Citation
To cite the u5mr package in publications use
@Manual{Myo2020space,
title = {Under-Five Child Mortality Estimation using the R Package u5mr},
author = {Myo Minn Oo},
year = {2021}
}
Please note that this project is looking for contributors. By participating in this project, you agree to abide by its terms with Contributor Code of Conduct, version 1.0.0, available at https://www.contributor-covenant.org/version/1/0/0/code-of-conduct/.