This R package was developed as part of the Sheffield Tobacco and Alcohol Policy Modelling https://stapm.gitlab.io/ by the School of Health and Related Research (ScHARR) at the University of Sheffield.
The aim of the research programme is to identify and evaluate approaches to reducing the harm from tobacco and alcohol, with the aim of improving commissioning in a public health policy context, i.e. providing knowledge to support benefits achieved by policymakers.
The two objectives of the research programme are:
- To evaluate the health and economic effects of past trends, policy changes or interventions that have affected alcohol consumption and/or tobacco smoking
- To appraise the health and economic outcomes of potential future trends, changes to alcohol and/or tobacco policy or new interventions
The STAPM modelling is not linked to the tobacco or alcohol industry and is conducted without industry funding or influence.
The code has been made open source for the following two reasons:
- Transparency. Open science, allowing review and feedback to the project team on the code and methods used.
- Methodology sharing. For people to understand the code and methods used so they might use aspects of it in their own work, e.g., because they are doing something partially related that isn’t exactly the same job and might like to ‘dip into’ elements of this code for inspiration.
The code is actively being used in project work. It is being reviewed and developed all the time; more tests and checks are being added.
The repository is not intended to be maintained by an open source community wider than the development team.
The mortality microdata that this R package is designed to process was obtained through data access requests to the Office for National Statistics (for English and Welsh data) and National Records Scotland.
In ScHARR, all data is stored and processed according to the ScHARR Information Governance Policy. No mortality microdata is included within this package.
Data checks are brief reports that show the results of the mortality data processing using the mort.tools package. The data checks help to visualise the patterns in the data.
The code on Github (https://github.com/STAPM/mort.tools) is a mirror of the code in a private Gitlab repository where the actual development takes place (https://gitlab.com/stapm/r-packages/mort.tools). The code in the Github repository is linked to a repository on the Open Science Framework, which provides the doi for the package citation (https://osf.io/wn6rh/).
Gillespie D, Webster L, Angus C, Brennan A ([YEAR]). mort.tools: An R Package for Processing Mortality Microdata. R package version [x.x.x]. University of Sheffield. https://stapm.gitlab.io/r-packages/mort.tools/. doi: https://doi.org/10.17605/OSF.IO/WN6RH
The motivation for mort.tools was to develop a set of functions to
clean and do basic processing of the data. The advantage of this is that
it ensures we apply a consistent approach across years of data, data
from different countries, and across the different projects for which we
use the data.
mort.tools was created as part of a programme of work on the health
economics of tobacco and alcohol at the School of Health and Related
Research (ScHARR), The University of Sheffield. This programme is based
around the construction of the Sheffield Tobacco and Alcohol Policy
Model (STAPM), which aims to use comparable methodologies to evaluate
the impacts of tobacco and alcohol policies, and investigate the
consequences of clustering and interactions between tobacco and alcohol
consumption behaviours.
mort.tools works with data on annual counts of deaths and mid-year population sizes. Counts of deaths are grouped according to the set of tobacco and alcohol related diseases considered in the modelling (see https://osf.io/v945r/). Deaths not falling into one of these classifications are categorised as ‘all other causes’. Counts of deaths and mid-year population sizes are stratified by single years of age, sex and Index of Multiple Deprivation quintiles. For England, further stratification by local authority was included in the data request to the Office for National Statistics. The mort.tools R package does not function without this data. However no datasets are provisioned. Data must therefore be obtained by data requests to the Office for National Statistics or National Records Scotland.
The package is primarily designed for users at the University of Sheffield, working off the university’s networked drives and in the secure virtual machine data environment. This is where most of the testing has taken place so there might be unexpected issues out of that environment.
What the software does in general and how it relates to data is documented in the vignettes under “Methods and data checks”.
See Get started for example workflows. We have written a range of short methods descriptions. There is a reference list of functions in this package. Data processing is done on the University of Sheffield managed secure virtual machine data environment.
The suite of functions within mort.tools reads the data for each year,
produces cause-specific life tables (focusing on the diseases related to
tobacco and alcohol), and gives options for conducting forecasts of
cause-specific death rates. The methods for forecasting are still under
development and subject to review.
The inputs are the counts of death and mid-year population size.
The processes applied by the functions in mort.tools give options
to:
- Read each year of data.
- Group the data by the causes related to tobacco and alcohol.
- Construct cause-specific life tables.
- Forecast trends in cause-specific mortality.
The output of these processes are the trends in cause-specific death
rates and population sizes. These dataset can be saved so that you don’t
need to run the cleaning processes in mort.tools each time you want to
use the cleaned data.
mort.tools is publicly available via Github.
By default the user should install the latest tagged version of the package. Otherwise, if you want to reproduce project work and know the version of the package used, install that version.
If on a University of Sheffield managed computer, install the R, RStudio
and Rtools bundle from the Software Centre. Install Rtools - using the
installr
package can make this easier. Then install the latest or a specified
version of mort.tools from Github with:
#install.packages("devtools")
# The tobalcepi STAPM R package is also required
devtools::install_git(
"https://github.com/stapm/tobalcepi.git",
ref = "x.x.x",
build_vignettes = FALSE)
devtools::install_git(
"https://github.com/stapm/mort.tools.git",
ref = "x.x.x",
build_vignettes = FALSE)
# ref = "x.x.x" is the version to install - change to the version you want e.g. "1.2.3"Or clone the package repo locally and use the ‘install and restart’ button in the Build tab of RStudio. This option is more convenient when testing development versions.
Then load the package, and some other packages that are useful. Note
that the code within mort.tools uses the data.table::data.table()
syntax.
# Load the package
library(data.table)
library(mort.tools)
library(tobalcepi)
# Other useful packages
library(dplyr) # for data manipulation and summary
library(magrittr) # for pipes
library(ggplot2) # for plotting