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README.Rmd
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README.Rmd
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---
output:
github_document:
toc: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# Decomposition of the US black/white inequality in premature mortality, , 2010–2015: an observational study
<p align="center">
```{r echo=FALSE, out.width = "650px", fig.align='center'}
knitr::include_graphics("./plots/header.jpg")
```
</p>
# Introduction
Code for our *BMJ Open* paper ["Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study"](http://dx.doi.org/10.1136/bmjopen-2019-029373). This paper uses joint Bayesian spatial models with [restricted-access compressed mortality data](https://www.cdc.gov/nchs/data_access/cmf.htm) to estimate and decompose black/white inequalities in premature mortality. The full citation is:
> Kiang MV, Krieger N, Buckee CO, Onnela JP, & Chen JT, Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study, *BMJ Open* (December 2019), doi: [10.1136/bmjopen-2019-029373](http://dx.doi.org/10.1136/bmjopen-2019-029373)
## Issues
Please submit issues [via Github](https://github.com/mkiang/decomposing_inequality/issues) or via email.
## Important note about reproducibility
Due to limitations on sharing the restricted-access data, this pipeline is not fully reproducible. To reproduce the pipeline, you must have [the restricted-access compressed mortality files](https://www.cdc.gov/nchs/data_access/cmf.htm) and the accompanying population estimates. Specify the path to these files in the [`04_extracting_cmf_data.R`](https://github.com/mkiang/decomposing_inequality/blob/master/code/04_extracting_cmf_data.R#L64) script in lines 64 and 72.
# Disclaimer
Since completion of this project, there have been significant advances in the underlying software packages. For example, `stan` [now allows for parallel processing *within a single chain*](https://github.com/stan-dev/math/wiki/Threading-Support), which should substantially reduce computation time. In addition, there has been significant development in estimating a spatial conditional autoregressive using `stan` (e.g., [`cor_car()`](https://rdrr.io/cran/brms/man/cor_car.html) in the `brms` package). Members of the `stan` team themselves have [published an example](https://doi.org/10.1016/j.sste.2019.100301) that did not exist when this project was underway.
All that to say, this code should be considered a **starting point** for future project development. Modernizing the code used in this project will likely decrease the computational burden substantially.
# Requirements
## Restricted-access compressed mortality files
Request access to the compressed mortality files through the [National Center for Health Statistics](https://www.cdc.gov/nchs/data_access/cmf.htm).
## Software
All analyses are conducted using `R`.
- `R` can be [downloaded via CRAN](https://cran.r-project.org/).
- In addition, we highly recommend the use of [RStudio](https://www.rstudio.com/products/rstudio/download/) when running `R`.
# Analysis pipeline
The code is made to be run in sequential order.
Intermediate, publicly-available files (e.g., the ACS variables and shapefiles) are included, so you should not need to update `config.yml`. However, if you want to use other ACS variables, or start from scratch, you will need to [request an API key](https://api.data.gov/signup/) from the US Census Bureau and put it in the `config.yml` file. See `./config.yml` for descriptions of project-wide parameters that can be modified.
Code files beginning with `50_` were used internally for diagnostic purposes but not included in the final manuscript.
# Acknowledgement
Special thanks to [Max Joseph](https://mbjoseph.github.io/) who freely provided his [`stan` conditional autoregressive code](https://github.com/mbjoseph/CARstan) online ([doi: 10.5281/zenodo.210407](https://doi.org/10.5281/zenodo.210407)).
Interested readers should see his [official `stan` case study](https://mc-stan.org/users/documentation/case-studies/mbjoseph-CARStan.html), another [ICAR `stan` case study here](https://mc-stan.org/users/documentation/case-studies/icar_stan.html), and a [paper using ICAR by members of the `stan` team](https://doi.org/10.1016/j.sste.2019.100301).
# Authors
- [Mathew Kiang](https://mathewkiang.com) (: [mkiang](https://github.com/mkiang) | : [\@mathewkiang](https://twitter.com/mathewkiang))
- [Nancy Krieger](https://www.hsph.harvard.edu/nancy-krieger/)
- [Caroline Buckee](https://www.hsph.harvard.edu/caroline-buckee/) (: [\@Caroline_OF_B](https://twitter.com/Caroline_OF_B))
- [Jukka-Pekka Onnela](https://www.hsph.harvard.edu/onnela-lab/) (: [jponnela](https://github.com/jponnela) | : [\@jponnela](https://twitter.com/jponnela))
- [Jarvis Chen](http://www.dfhcc.harvard.edu/insider/member-detail/member/jarvis-t-chen-scd/)