AdultCoverage

This repository contains R code for a technical paper in progress, provisionally titled "R implementations of three growth balance methods for estimating adult mortality coverage", with Everton Lima and Bernardo Queiroz. It is likely too early to cite, however you are free to see what we're up to and use (with attribution):

"R implementations of three growth balance methods for estimating adult mortality coverage" by Everton Lima, Bernardo Queiroz, and Timothy L. M. Riffe is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

DDM R package

This project has produced a small R package that implements three methods for indirect estimation of death registration coverage (Generalized Growth Balance, Synthetic Extinct Generations, and a hybrid of the two).

A short tutorial

To install from the central R repository (current version 1.0-0):

install.packages("DDM")

The development version (current version 1.0-0) hosted here on github is always the most up-to-date version. There are different ways to install the development version of the package.

Download the zip ball or tar ball, decompress and run R CMD INSTALL on the subfolder called R/DDM in the terminal command line, or (easier) use the devtools package to install the development version:

# install.packages("devtools")

library(devtools)
install_github("timriffe/AdultCoverage/AdultCoverage/R/DDM")

Then you can load the package using:

library(DDM)

Be aware that if you report a bug and we fix it, then you'll need to reinstall (from github) to get the changes.

Your data need to be in this kind of shape:

head(Moz)
cod    pop1   pop2  deaths age sex year1 year2
  1 1388350  1963660 88248   0   f  1997  2007
  1 1113675  1615244 11424   5   f  1997  2007
  1  878429  1183939  5677  10   f  1997  2007
  1  854078   991323  6123  15   f  1997  2007
  1  827614   986526  7280  20   f  1997  2007
  1  654465   841416  7212  25   f  1997  2007

Here cod indicates the group, a single year, sex, region of data that is to be tested. pop1 and pop2 are the first and second census, respectively. deaths can contain the average number of deaths in each age group in the intercensal period or it can contain the sum of the deaths in each age in the intercensal period. If you give the sum, then specify deaths.summed = TRUE in the arguments to any of the estimation functions. Otherwise the default is to treat deaths as the average. This could be a straight arithmetic average, or simply the average of the deaths observed around census 1 or census 2. For this later case, you'll need to average yourself beforehand, as deaths.summed = TRUE will only do the right thing if deaths over the whole intercensal period are given.

age should be the lower bound of five-year age groups (incl. age 0-4!). If you give standard abridged data (0,1,5), then the pop1, pop2, and deaths from ages 0 and 1 are automatically summed together into the infant category. Don't give single-age data at this time. We hope to add an abridgement function soon, though, to handle such data automatically. sex is character, either "f" or "m". Census dates can be conveyed in a variety of ways. If only year1 and year2 are given, we assume Jan 1. It is best to specify proper date classes and use date1, date2 as column names instead:

cod    pop1    pop2 deaths age sex      date1      date2
  1 1388350 1963660  88248   0   f 1997-08-01 2007-08-01
  1 1113675 1615244  11424   5   f 1997-08-01 2007-08-01
  1  878429 1183939   5677  10   f 1997-08-01 2007-08-01
  1  854078  991323   6123  15   f 1997-08-01 2007-08-01
  1  827614  986526   7280  20   f 1997-08-01 2007-08-01
  1  654465  841416   7212  25   f 1997-08-01 2007-08-01

Results are contingent on evaluating results for particular age ranges. In spreadsheets this is typically done visually, which a plot referenced to some cell range that the user could manipulate. Here, we have a function that works similarly, but you need to use it just for one data grouping at a time (cod):

my_ages <- ggbChooseAges(x[x$cod==1,])

This will open a graphics device, where you can interactively select age ranges by clicking on ages. When you are done, click in the margin to close the device, and it returns the vector of ages. You can use these, or any other vector of ages, to manually specify the age range that each method should use:

ggb(Moz, exact.ages = my_ages)
seg(Moz, exact.ages = my_ages)
ggbseg(Moz, exact.ages = my_ages)

By default these functions will pick a decent age-range on their own:

ggb(Moz)
seg(Moz)
ggbseg(Moz)

And the result will depend on the age-range chosen. If left to automatically choose age-ranges, the evaluation methods will pick one independently for each data grouping (cod). Let's say your data has a large number of groupings (regions, countries, intercensal periods, whatever). You can get a messy overview of results by running:

Results <- ddm(my.huge.data)
ddmplot(Results)

This overview plot also gives the harmonic mean of the coverage estimate given from the three methods provided.

What's missing?

This code is newish, and certainly changes will be made as users report issues or make requests. The next steps will include improvements to graphical diagnostics and an easier way to return the interim data object used to calculate results. Possibly in the future we would think about adding more methods, such as DDM methods that adjust for migration.