README.md
In mrc-ide/demogsurv: Demographic analysis of DHS and other household surveys
demogsurv
The goal of demogsurv is to:
- Calculate common demographic indicators from household survey data,
including child mortality, adult mortality, and fertility.
- Stratify indicators according to arbitrary age groups, calendar
periods, birth cohorts, time before survey, or other survey
variables (region, residence, wealth category, education, etc.).
- Efficiently prepare event / person-year datasets from survey data
for other model-based analysis.
- Provide standard error estimates for all indicators via Taylor
linearization or jackknife.
- Calculate sample covariance for indicators estimated from a single
complex survey (e.g. time-series of child mortality estimates).
- Default parameters are specified for analysis of DHS recode
datasets without fuss, but funciton
implementation is flexible for use with other survey data.
For analysis of DHS data, the package interacts well with
rdhs. See the
vignette
for an example.
Installation
You can install the development version from
GitHub with:
# install.packages("devtools")
devtools::install_github("mrc-ide/demogsurv")
The package will be released on CRAN in
due course.
Example
Load the package and example datasets created from DHS Model
Datasets.
library(demogsurv)
data(zzbr) # Births recode (child mortality)
data(zzir) # Individuals recode (fertility, adult mortality)
Child mortality
By default, the function calc_nqx calculates U5MR by periods 0-4, 5-9,
and 10-14 years before the survey. Before calculating mortality rates,
create a binary variable indicator whether a death occurred and a
variable giving the date of death, placed 0.5 months in the month the
death occurred.
zzbr$death <- zzbr$b5 == "no" # b5: child is alive ("yes" or "no")
zzbr$dod <- zzbr$b3 + zzbr$b7 + 0.5
u5mr <- calc_nqx(zzbr)
u5mr
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
Note that calc_nqx() does not reproduce child mortality estimates
produced in DHS reports. calc_nqx() conducts a standard demographic
rate calculation based on observed events and person years within each
age group and then converts the cumulative hazard to survival
probabilities. The standard DHS indicator uses a rule-based approach to
allocate child deaths and person years across age groups and proceeds by
calculating direct probabilities of death in each age group (see
Rutstein and
Rojas 2006).
A function calc_dhs_u5mr() will reproduce the DHS calculation, but is
not yet fully implemented.
Use the argument by= to specify factor variables by which to stratify
the rate calculation.
calc_nqx(zzbr, by=~v102) # by urban/rural residence
#> v102 tips est se ci_l ci_u
#> 1 urban 10-14 0.1750790 0.018595935 0.1378145 0.2107329
#> 2 rural 10-14 0.2453074 0.013657991 0.2180578 0.2716074
#> 3 urban 5-9 0.1818306 0.016617688 0.1486035 0.2137609
#> 4 rural 5-9 0.1994068 0.009424996 0.1807194 0.2176679
#> 5 urban 0-4 0.1532941 0.014183345 0.1250339 0.1806416
#> 6 rural 0-4 0.1345284 0.006453178 0.1217876 0.1470845
calc_nqx(zzbr, by=~v190, tips=c(0, 10)) # by wealth quintile, 0-9 years before
#> v190 tips est se ci_l ci_u
#> 1 poorest 0-9 0.1768813 0.009976478 0.1570936 0.1962044
#> 2 poorer 0-9 0.1823974 0.009763729 0.1630352 0.2013118
#> 3 middle 0-9 0.1657497 0.010603135 0.1447069 0.1862747
#> 4 richer 0-9 0.1557011 0.012229238 0.1313887 0.1793329
#> 5 richest 0-9 0.1456974 0.015916788 0.1139245 0.1763310
calc_nqx(zzbr, by=~v101+v102, tips=c(0, 10)) # by region and residence
#> v101 v102 tips est se ci_l ci_u
#> 1 region 1 urban 0-9 0.1440943 0.01331266 0.1176001 0.1697929
#> 2 region 2 urban 0-9 0.1648417 0.02741923 0.1093342 0.2168898
#> 3 region 3 urban 0-9 0.1618804 0.01696421 0.1279628 0.1944787
#> 4 region 4 urban 0-9 0.1998386 0.03390294 0.1305530 0.2636029
#> 5 region 1 rural 0-9 0.1559257 0.01004546 0.1360056 0.1753866
#> 6 region 2 rural 0-9 0.1755639 0.01088753 0.1539462 0.1966293
#> 7 region 3 rural 0-9 0.2021511 0.03301796 0.1347402 0.2643101
#> 8 region 4 rural 0-9 0.1764668 0.01299817 0.1505927 0.2015527
The sample covariance or correlation matrix of the estimates can be
obtained via vcov().
vcov(u5mr) # sample covariance
#> [,1] [,2] [,3]
#> [1,] 1.375972e-04 4.450180e-05 8.842864e-06
#> [2,] 4.450180e-05 6.876656e-05 1.451310e-05
#> [3,] 8.842864e-06 1.451310e-05 4.138089e-05
cov2cor(vcov(u5mr)) # sample correlation
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.4574926 0.1171894
#> [2,] 0.4574926 1.0000000 0.2720644
#> [3,] 0.1171894 0.2720644 1.0000000
Standard error estimation can be done via Taylor linearisation,
unstratified jackknife, or stratified jackknife. Results are very
similar.
calc_nqx(zzbr, varmethod = "lin") # default is linearization
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
calc_nqx(zzbr, varmethod = "jkn") # stratified jackknife (varmethod = "jkn")
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011931233 0.1974762 0.2442815
#> 2 5-9 0.1937855 0.008280170 0.1773984 0.2098463
#> 3 0-4 0.1408711 0.006434272 0.1281716 0.1533856
## Compare unstratified standard error estimates for linearization and jackknife
calc_nqx(zzbr, strata=NULL, varmethod = "lin") # unstratified design
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011976980 0.1973986 0.2443546
#> 2 5-9 0.1937855 0.008367844 0.1772169 0.2100205
#> 3 0-4 0.1408711 0.006507844 0.1280208 0.1535320
calc_nqx(zzbr, strata=NULL, varmethod = "jk1") # unstratififed jackknife
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.012088926 0.1971571 0.2445818
#> 2 5-9 0.1937855 0.008408382 0.1771422 0.2100923
#> 3 0-4 0.1408711 0.006539720 0.1279620 0.1535891
To calculate different child mortality indicators (neonatal, infant,
etc.), specify different age groups over which to aggregate.
calc_nqx(zzbr, agegr=c(0, 1)/12) # neonatal
#> tips est se ci_l ci_u
#> 1 10-14 0.04358764 0.004262693 0.03519631 0.05190598
#> 2 5-9 0.04590724 0.003799869 0.03843049 0.05332585
#> 3 0-4 0.03792144 0.003320563 0.03139119 0.04440766
calc_nqx(zzbr, agegr=c(1, 3, 5, 12)/12) # postneonatal
#> tips est se ci_l ci_u
#> 1 10-14 0.10613868 0.007294180 0.09172741 0.12032129
#> 2 5-9 0.08366400 0.006097552 0.07163475 0.09553739
#> 3 0-4 0.05107115 0.003999245 0.04320031 0.05887724
calc_nqx(zzbr, agegr=c(0, 1, 3, 5, 12)/12) # infant (1q0)
#> tips est se ci_l ci_u
#> 1 10-14 0.1451000 0.008870650 0.1275358 0.16231054
#> 2 5-9 0.1257305 0.006823068 0.1122547 0.13900166
#> 3 0-4 0.0870559 0.005070203 0.0770642 0.09693942
calc_nqx(zzbr, agegr=c(12, 24, 36, 48, 60)/12) # child (4q1)
#> tips est se ci_l ci_u
#> 1 10-14 0.08905182 0.007189004 0.07485210 0.10303360
#> 2 5-9 0.07784223 0.005665474 0.06667098 0.08887977
#> 3 0-4 0.05894689 0.004529373 0.05002747 0.06778255
calc_nqx(zzbr, agegr=c(0, 1, 3, 5, 12, 24, 36, 48, 60)/12) # u5mr (5q0)
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
Calculate annual 5q0 by calendar year (rather than
years preceding survey).
calc_nqx(zzbr, period=2005:2015, tips=NULL)
#> period est se ci_l ci_u
#> 1 2005 0.1937695 0.01611970 0.16154821 0.2247525
#> 2 2006 0.1890020 0.01485224 0.15936341 0.2175956
#> 3 2007 0.1983320 0.01469757 0.16900148 0.2266273
#> 4 2008 0.1906183 0.01306713 0.16459769 0.2158285
#> 5 2009 0.1979731 0.01559065 0.16682639 0.2279554
#> 6 2010 0.1874172 0.01366295 0.16019216 0.2137596
#> 7 2011 0.1768661 0.01378438 0.14940096 0.2034444
#> 8 2012 0.1390004 0.01488779 0.10932074 0.1676910
#> 9 2013 0.1224668 0.01183607 0.09895918 0.1453611
#> 10 2014 0.1225079 0.01208800 0.09849310 0.1458829
Adult mortality
The function calc_nqx() can also used to calculate adult mortality
indicators such as 35q15. First, the convenience
function reshape_sib_data() transforms respondent-level data to a
dataset with one row for each sibling reported. Then define a binary
variable for whether the sibling is alive or dead.
zzsib <- reshape_sib_data(zzir)
zzsib$death <- factor(zzsib$mm2, c("dead", "alive")) == "dead"
Calculate 35q15 for the seven year period before
the survey.
calc_nqx(zzsib, agegr=seq(15, 50, 5), tips=c(0, 7), dob="mm4", dod="mm8")
#> tips est se ci_l ci_u
#> 1 0-6 0.1778199 0.009395366 0.1591976 0.1960298
Calculate 35q15 by sex, replicating Table MM2.2.
zzsib$sex <- factor(zzsib$mm1, c("female", "male")) # drop mm2 = 3: "missing"
calc_nqx(zzsib, by=~sex, agegr=seq(15, 50, 5), tips=c(0, 7), dob="mm4", dod="mm8")
#> sex tips est se ci_l ci_u
#> 1 female 0-6 0.1790557 0.01296694 0.1532435 0.2040811
#> 2 male 0-6 0.1766238 0.01332997 0.1500787 0.2023399
This calculation exactly reproduces the 35q15
estiamtes produced for Table MM2 for DHS reports. Additional
functionality will be added in future for producing ASMRs (Table MM1),
MMR, and PM (Table MM3) will be added in future.
Fertility
The functions calc_asfr() and calc_tfr() calculate age-specific
fertility rates and total fertility rate, respectively. The default
calculation is by five-year age groups for three years before the
survey, exactly reproducing the estimates produced in DHS reports.
## Replicate DHS Table 5.1.
## Total ASFR and TFR in 3 years preceding survey
calc_asfr(zzir, tips=c(0, 3))
#> agegr tips asfr se_asfr
#> 1 15-19 0-2 0.11901592 0.008154144
#> 2 20-24 0-2 0.20736603 0.012782416
#> 3 25-29 0-2 0.21553394 0.008245023
#> 4 30-34 0-2 0.18803561 0.010426419
#> 5 35-39 0-2 0.12494212 0.008135705
#> 6 40-44 0-2 0.06044451 0.007502008
#> 7 45-49 0-2 0.02828233 0.006066635
calc_tfr(zzir)
#> tips tfr se_tfr
#> 1 0-2 4.718102 0.1949224
## ASFR and TFR by urban/rural residence
reshape2::dcast(calc_asfr(zzir, ~v025, tips=c(0, 3)), agegr ~ v025, value.var = "asfr")
#> agegr urban rural
#> 1 15-19 0.07718083 0.16269262
#> 2 20-24 0.15883881 0.25754199
#> 3 25-29 0.16862119 0.24797353
#> 4 30-34 0.14294124 0.21875542
#> 5 35-39 0.08182797 0.15262614
#> 6 40-44 0.04817726 0.07005696
#> 7 45-49 0.02404944 0.03087701
calc_tfr(zzir, by=~v025)
#> v025 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.2813637
#> 2 rural 0-2 5.702618 0.1425967
calc_tfr(zzir, by=~v025, varmethod="jkn")
#> v025 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.3004737
#> 2 rural 0-2 5.702618 0.1427030
Replicate fertility estimates stratified by various sociodemographic
characteristics.
## Replicate DHS Table 5.2
calc_tfr(zzir, ~v102) # residence
#> v102 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.2813637
#> 2 rural 0-2 5.702618 0.1425967
calc_tfr(zzir, ~v101) # region
#> v101 tips tfr se_tfr
#> 1 region 1 0-2 5.334781 0.1781935
#> 2 region 2 0-2 5.255445 0.2842471
#> 3 region 3 0-2 3.079052 0.3674651
#> 4 region 4 0-2 5.500077 0.2452335
calc_tfr(zzir, ~v106) # education
#> v106 tips tfr se_tfr
#> 1 no education 0-2 5.585398 0.1406657
#> 2 primary 0-2 5.041455 0.3599601
#> 3 secondary 0-2 3.245211 0.2956154
#> 4 higher 0-2 1.388428 0.3198573
calc_tfr(zzir, ~v190) # wealth quintile
#> v190 tips tfr se_tfr
#> 1 poorest 0-2 5.951668 0.2183868
#> 2 poorer 0-2 5.693275 0.1867203
#> 3 middle 0-2 5.270892 0.2283221
#> 4 richer 0-2 4.406202 0.2813975
#> 5 richest 0-2 2.907027 0.2738504
calc_tfr(zzir) # total
#> tips tfr se_tfr
#> 1 0-2 4.718102 0.1949224
Generate estimates stratified by both calendar period and time preceding
survey.
calc_tfr(zzir, period = c(2010, 2013, 2015), tips=0:5)
#> period tips tfr se_tfr
#> 1 2010-2012 4 5.260048 0.3166023
#> 2 2010-2012 3 5.207062 0.2697222
#> 3 2010-2012 2 4.084194 0.3176187
#> 4 2013-2014 2 5.020537 0.2999262
#> 5 2013-2014 1 4.555710 0.2448427
#> 6 2013-2014 0 4.720417 0.3525991
Calculate ASFR by birth cohort.
asfr_coh <- calc_asfr(zzir, cohort=c(1980, 1985, 1990, 1995), tips=NULL)
reshape2::dcast(asfr_coh, agegr ~ cohort, value.var = "asfr")
#> agegr 1980-1984 1985-1989 1990-1994
#> 1 15-19 0.1636472 0.1641461 0.1470502
#> 2 20-24 0.2610719 0.2402068 0.2140443
#> 3 25-29 0.2519899 0.2321897 0.2957371
#> 4 30-34 0.2012103 0.1674921 NA
#> 5 35-39 0.1473996 NA NA
To Do
-
Refactor for single calc_rate() function used by mortality and
fertility calculations.
-
Add indirect indicators
-
Implement DHS child mortality calculation (Rutstein and Rojas 2006).
-
Handle married women only datasets.
-
Calculate multiple aggregations of nqx and sample correlation of
these (e.g. NN, PN, 1q0, 4q1, 5q0).
-
Document everything…
-
Develop, test, and create examples for non-DHS household surveys,
e.g. MICS, WFS.
-
Extend interface to allow specification of formula, variable name,
or vectors.
-
Add some basic data checking before rate calculation, good warnings,
potentially automatic handling. [ ] Variable names all exist. [
] No missing data in date variables. [ ] Episode start date is
before episode end date. [ ] All birth history ids appear in
respondent dataset.
mrc-ide/demogsurv documentation built on March 21, 2022, 9:49 p.m.
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demogsurv
The goal of demogsurv is to:
- Calculate common demographic indicators from household survey data, including child mortality, adult mortality, and fertility.
- Stratify indicators according to arbitrary age groups, calendar periods, birth cohorts, time before survey, or other survey variables (region, residence, wealth category, education, etc.).
- Efficiently prepare event / person-year datasets from survey data for other model-based analysis.
- Provide standard error estimates for all indicators via Taylor linearization or jackknife.
- Calculate sample covariance for indicators estimated from a single complex survey (e.g. time-series of child mortality estimates).
- Default parameters are specified for analysis of DHS recode datasets without fuss, but funciton implementation is flexible for use with other survey data.
For analysis of DHS data, the package interacts well with
rdhs. See the
vignette
for an example.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("mrc-ide/demogsurv")
The package will be released on CRAN in due course.
Example
Load the package and example datasets created from DHS Model Datasets.
library(demogsurv)
data(zzbr) # Births recode (child mortality)
data(zzir) # Individuals recode (fertility, adult mortality)
Child mortality
By default, the function calc_nqx calculates U5MR by periods 0-4, 5-9,
and 10-14 years before the survey. Before calculating mortality rates,
create a binary variable indicator whether a death occurred and a
variable giving the date of death, placed 0.5 months in the month the
death occurred.
zzbr$death <- zzbr$b5 == "no" # b5: child is alive ("yes" or "no")
zzbr$dod <- zzbr$b3 + zzbr$b7 + 0.5
u5mr <- calc_nqx(zzbr)
u5mr
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
Note that calc_nqx() does not reproduce child mortality estimates
produced in DHS reports. calc_nqx() conducts a standard demographic
rate calculation based on observed events and person years within each
age group and then converts the cumulative hazard to survival
probabilities. The standard DHS indicator uses a rule-based approach to
allocate child deaths and person years across age groups and proceeds by
calculating direct probabilities of death in each age group (see
Rutstein and
Rojas 2006).
A function calc_dhs_u5mr() will reproduce the DHS calculation, but is
not yet fully implemented.
Use the argument by= to specify factor variables by which to stratify
the rate calculation.
calc_nqx(zzbr, by=~v102) # by urban/rural residence
#> v102 tips est se ci_l ci_u
#> 1 urban 10-14 0.1750790 0.018595935 0.1378145 0.2107329
#> 2 rural 10-14 0.2453074 0.013657991 0.2180578 0.2716074
#> 3 urban 5-9 0.1818306 0.016617688 0.1486035 0.2137609
#> 4 rural 5-9 0.1994068 0.009424996 0.1807194 0.2176679
#> 5 urban 0-4 0.1532941 0.014183345 0.1250339 0.1806416
#> 6 rural 0-4 0.1345284 0.006453178 0.1217876 0.1470845
calc_nqx(zzbr, by=~v190, tips=c(0, 10)) # by wealth quintile, 0-9 years before
#> v190 tips est se ci_l ci_u
#> 1 poorest 0-9 0.1768813 0.009976478 0.1570936 0.1962044
#> 2 poorer 0-9 0.1823974 0.009763729 0.1630352 0.2013118
#> 3 middle 0-9 0.1657497 0.010603135 0.1447069 0.1862747
#> 4 richer 0-9 0.1557011 0.012229238 0.1313887 0.1793329
#> 5 richest 0-9 0.1456974 0.015916788 0.1139245 0.1763310
calc_nqx(zzbr, by=~v101+v102, tips=c(0, 10)) # by region and residence
#> v101 v102 tips est se ci_l ci_u
#> 1 region 1 urban 0-9 0.1440943 0.01331266 0.1176001 0.1697929
#> 2 region 2 urban 0-9 0.1648417 0.02741923 0.1093342 0.2168898
#> 3 region 3 urban 0-9 0.1618804 0.01696421 0.1279628 0.1944787
#> 4 region 4 urban 0-9 0.1998386 0.03390294 0.1305530 0.2636029
#> 5 region 1 rural 0-9 0.1559257 0.01004546 0.1360056 0.1753866
#> 6 region 2 rural 0-9 0.1755639 0.01088753 0.1539462 0.1966293
#> 7 region 3 rural 0-9 0.2021511 0.03301796 0.1347402 0.2643101
#> 8 region 4 rural 0-9 0.1764668 0.01299817 0.1505927 0.2015527
The sample covariance or correlation matrix of the estimates can be
obtained via vcov().
vcov(u5mr) # sample covariance
#> [,1] [,2] [,3]
#> [1,] 1.375972e-04 4.450180e-05 8.842864e-06
#> [2,] 4.450180e-05 6.876656e-05 1.451310e-05
#> [3,] 8.842864e-06 1.451310e-05 4.138089e-05
cov2cor(vcov(u5mr)) # sample correlation
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.4574926 0.1171894
#> [2,] 0.4574926 1.0000000 0.2720644
#> [3,] 0.1171894 0.2720644 1.0000000
Standard error estimation can be done via Taylor linearisation, unstratified jackknife, or stratified jackknife. Results are very similar.
calc_nqx(zzbr, varmethod = "lin") # default is linearization
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
calc_nqx(zzbr, varmethod = "jkn") # stratified jackknife (varmethod = "jkn")
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011931233 0.1974762 0.2442815
#> 2 5-9 0.1937855 0.008280170 0.1773984 0.2098463
#> 3 0-4 0.1408711 0.006434272 0.1281716 0.1533856
## Compare unstratified standard error estimates for linearization and jackknife
calc_nqx(zzbr, strata=NULL, varmethod = "lin") # unstratified design
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011976980 0.1973986 0.2443546
#> 2 5-9 0.1937855 0.008367844 0.1772169 0.2100205
#> 3 0-4 0.1408711 0.006507844 0.1280208 0.1535320
calc_nqx(zzbr, strata=NULL, varmethod = "jk1") # unstratififed jackknife
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.012088926 0.1971571 0.2445818
#> 2 5-9 0.1937855 0.008408382 0.1771422 0.2100923
#> 3 0-4 0.1408711 0.006539720 0.1279620 0.1535891
To calculate different child mortality indicators (neonatal, infant, etc.), specify different age groups over which to aggregate.
calc_nqx(zzbr, agegr=c(0, 1)/12) # neonatal
#> tips est se ci_l ci_u
#> 1 10-14 0.04358764 0.004262693 0.03519631 0.05190598
#> 2 5-9 0.04590724 0.003799869 0.03843049 0.05332585
#> 3 0-4 0.03792144 0.003320563 0.03139119 0.04440766
calc_nqx(zzbr, agegr=c(1, 3, 5, 12)/12) # postneonatal
#> tips est se ci_l ci_u
#> 1 10-14 0.10613868 0.007294180 0.09172741 0.12032129
#> 2 5-9 0.08366400 0.006097552 0.07163475 0.09553739
#> 3 0-4 0.05107115 0.003999245 0.04320031 0.05887724
calc_nqx(zzbr, agegr=c(0, 1, 3, 5, 12)/12) # infant (1q0)
#> tips est se ci_l ci_u
#> 1 10-14 0.1451000 0.008870650 0.1275358 0.16231054
#> 2 5-9 0.1257305 0.006823068 0.1122547 0.13900166
#> 3 0-4 0.0870559 0.005070203 0.0770642 0.09693942
calc_nqx(zzbr, agegr=c(12, 24, 36, 48, 60)/12) # child (4q1)
#> tips est se ci_l ci_u
#> 1 10-14 0.08905182 0.007189004 0.07485210 0.10303360
#> 2 5-9 0.07784223 0.005665474 0.06667098 0.08887977
#> 3 0-4 0.05894689 0.004529373 0.05002747 0.06778255
calc_nqx(zzbr, agegr=c(0, 1, 3, 5, 12, 24, 36, 48, 60)/12) # u5mr (5q0)
#> tips est se ci_l ci_u
#> 1 10-14 0.2212304 0.011730185 0.1978969 0.2438851
#> 2 5-9 0.1937855 0.008292561 0.1773675 0.2098759
#> 3 0-4 0.1408711 0.006432798 0.1281701 0.1533871
Calculate annual 5q0 by calendar year (rather than years preceding survey).
calc_nqx(zzbr, period=2005:2015, tips=NULL)
#> period est se ci_l ci_u
#> 1 2005 0.1937695 0.01611970 0.16154821 0.2247525
#> 2 2006 0.1890020 0.01485224 0.15936341 0.2175956
#> 3 2007 0.1983320 0.01469757 0.16900148 0.2266273
#> 4 2008 0.1906183 0.01306713 0.16459769 0.2158285
#> 5 2009 0.1979731 0.01559065 0.16682639 0.2279554
#> 6 2010 0.1874172 0.01366295 0.16019216 0.2137596
#> 7 2011 0.1768661 0.01378438 0.14940096 0.2034444
#> 8 2012 0.1390004 0.01488779 0.10932074 0.1676910
#> 9 2013 0.1224668 0.01183607 0.09895918 0.1453611
#> 10 2014 0.1225079 0.01208800 0.09849310 0.1458829
Adult mortality
The function calc_nqx() can also used to calculate adult mortality
indicators such as 35q15. First, the convenience
function reshape_sib_data() transforms respondent-level data to a
dataset with one row for each sibling reported. Then define a binary
variable for whether the sibling is alive or dead.
zzsib <- reshape_sib_data(zzir)
zzsib$death <- factor(zzsib$mm2, c("dead", "alive")) == "dead"
Calculate 35q15 for the seven year period before the survey.
calc_nqx(zzsib, agegr=seq(15, 50, 5), tips=c(0, 7), dob="mm4", dod="mm8")
#> tips est se ci_l ci_u
#> 1 0-6 0.1778199 0.009395366 0.1591976 0.1960298
Calculate 35q15 by sex, replicating Table MM2.2.
zzsib$sex <- factor(zzsib$mm1, c("female", "male")) # drop mm2 = 3: "missing"
calc_nqx(zzsib, by=~sex, agegr=seq(15, 50, 5), tips=c(0, 7), dob="mm4", dod="mm8")
#> sex tips est se ci_l ci_u
#> 1 female 0-6 0.1790557 0.01296694 0.1532435 0.2040811
#> 2 male 0-6 0.1766238 0.01332997 0.1500787 0.2023399
This calculation exactly reproduces the 35q15 estiamtes produced for Table MM2 for DHS reports. Additional functionality will be added in future for producing ASMRs (Table MM1), MMR, and PM (Table MM3) will be added in future.
Fertility
The functions calc_asfr() and calc_tfr() calculate age-specific
fertility rates and total fertility rate, respectively. The default
calculation is by five-year age groups for three years before the
survey, exactly reproducing the estimates produced in DHS reports.
## Replicate DHS Table 5.1.
## Total ASFR and TFR in 3 years preceding survey
calc_asfr(zzir, tips=c(0, 3))
#> agegr tips asfr se_asfr
#> 1 15-19 0-2 0.11901592 0.008154144
#> 2 20-24 0-2 0.20736603 0.012782416
#> 3 25-29 0-2 0.21553394 0.008245023
#> 4 30-34 0-2 0.18803561 0.010426419
#> 5 35-39 0-2 0.12494212 0.008135705
#> 6 40-44 0-2 0.06044451 0.007502008
#> 7 45-49 0-2 0.02828233 0.006066635
calc_tfr(zzir)
#> tips tfr se_tfr
#> 1 0-2 4.718102 0.1949224
## ASFR and TFR by urban/rural residence
reshape2::dcast(calc_asfr(zzir, ~v025, tips=c(0, 3)), agegr ~ v025, value.var = "asfr")
#> agegr urban rural
#> 1 15-19 0.07718083 0.16269262
#> 2 20-24 0.15883881 0.25754199
#> 3 25-29 0.16862119 0.24797353
#> 4 30-34 0.14294124 0.21875542
#> 5 35-39 0.08182797 0.15262614
#> 6 40-44 0.04817726 0.07005696
#> 7 45-49 0.02404944 0.03087701
calc_tfr(zzir, by=~v025)
#> v025 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.2813637
#> 2 rural 0-2 5.702618 0.1425967
calc_tfr(zzir, by=~v025, varmethod="jkn")
#> v025 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.3004737
#> 2 rural 0-2 5.702618 0.1427030
Replicate fertility estimates stratified by various sociodemographic characteristics.
## Replicate DHS Table 5.2
calc_tfr(zzir, ~v102) # residence
#> v102 tips tfr se_tfr
#> 1 urban 0-2 3.508184 0.2813637
#> 2 rural 0-2 5.702618 0.1425967
calc_tfr(zzir, ~v101) # region
#> v101 tips tfr se_tfr
#> 1 region 1 0-2 5.334781 0.1781935
#> 2 region 2 0-2 5.255445 0.2842471
#> 3 region 3 0-2 3.079052 0.3674651
#> 4 region 4 0-2 5.500077 0.2452335
calc_tfr(zzir, ~v106) # education
#> v106 tips tfr se_tfr
#> 1 no education 0-2 5.585398 0.1406657
#> 2 primary 0-2 5.041455 0.3599601
#> 3 secondary 0-2 3.245211 0.2956154
#> 4 higher 0-2 1.388428 0.3198573
calc_tfr(zzir, ~v190) # wealth quintile
#> v190 tips tfr se_tfr
#> 1 poorest 0-2 5.951668 0.2183868
#> 2 poorer 0-2 5.693275 0.1867203
#> 3 middle 0-2 5.270892 0.2283221
#> 4 richer 0-2 4.406202 0.2813975
#> 5 richest 0-2 2.907027 0.2738504
calc_tfr(zzir) # total
#> tips tfr se_tfr
#> 1 0-2 4.718102 0.1949224
Generate estimates stratified by both calendar period and time preceding survey.
calc_tfr(zzir, period = c(2010, 2013, 2015), tips=0:5)
#> period tips tfr se_tfr
#> 1 2010-2012 4 5.260048 0.3166023
#> 2 2010-2012 3 5.207062 0.2697222
#> 3 2010-2012 2 4.084194 0.3176187
#> 4 2013-2014 2 5.020537 0.2999262
#> 5 2013-2014 1 4.555710 0.2448427
#> 6 2013-2014 0 4.720417 0.3525991
Calculate ASFR by birth cohort.
asfr_coh <- calc_asfr(zzir, cohort=c(1980, 1985, 1990, 1995), tips=NULL)
reshape2::dcast(asfr_coh, agegr ~ cohort, value.var = "asfr")
#> agegr 1980-1984 1985-1989 1990-1994
#> 1 15-19 0.1636472 0.1641461 0.1470502
#> 2 20-24 0.2610719 0.2402068 0.2140443
#> 3 25-29 0.2519899 0.2321897 0.2957371
#> 4 30-34 0.2012103 0.1674921 NA
#> 5 35-39 0.1473996 NA NA
To Do
-
Refactor for single
calc_rate()function used by mortality and fertility calculations. -
Add indirect indicators
-
Implement DHS child mortality calculation (Rutstein and Rojas 2006).
-
Handle married women only datasets.
-
Calculate multiple aggregations of nqx and sample correlation of these (e.g. NN, PN, 1q0, 4q1, 5q0).
-
Document everything…
-
Develop, test, and create examples for non-DHS household surveys, e.g. MICS, WFS.
-
Extend interface to allow specification of formula, variable name, or vectors.
-
Add some basic data checking before rate calculation, good warnings, potentially automatic handling. [ ] Variable names all exist. [ ] No missing data in date variables. [ ] Episode start date is before episode end date. [ ] All birth history ids appear in respondent dataset.
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