sia-vulnerable-mums-analysis

Overview

This repository contains code to perform exploratory analysis and identify natural clusters among mothers and their children along with key characteristics that describe each of these clusters. The objective of this analysis is to check whether there are distinctive patterns in the data that describe groups with risk of bad outcomes. Instead of relying purely on the presence or absence of a risk factor for a particular individual, we attempt to create fuzzy groupings of individuals that look quite similar to each other in terms of their characteristics, and then identify groups that register highly on risk factor variables. This work is part of exploratory analysis for identification of priority populations.

License for Vulnerable Mums Analysis Content

Crown copyright ©. This copyright work is licensed under the Creative Commons Attribution 4.0 International licence. In essence, you are free to copy, distribute and adapt the work, as long as you attribute the work to the New Zealand Government and abide by the other licence terms.

To view a copy of this licence, visit https://creativecommons.org/licenses/by-sa/4.0/.

Please note that neither the New Zealand Government emblem nor the New Zealand Government logo may be used in any way which infringes any provision of the Flags, Emblems, and Names Protection Act 1981 or would infringe such provision if the relevant use occurred within New Zealand. Attribution to the New Zealand Government should be in written form and not by reproduction of any emblem or the New Zealand Government logo.

License for Vulnerable Mums Analysis Code Base

GNU GPLv3 License

Crown copyright (c) 2017, Social Investment Agency on behalf of the New Zealand Government.

See for more details.

Dependencies

• It is necessary to have an IDI project if you wish to run the code. Visit the Stats NZ website for more information about this.

• Code dependencies are captured via submodules in this repository. You will find the submodules in the lib folder of this repository. To ensure you clone the submodules as well, use git clone --recursive https://github.com/nz-social-investment-agency/vulnerable_mothers.git. Regular cloning or downloading of the zip file will result in all the lib subfolders being empty. Currently the code dependencies for the vulnerable mothers repository are -

  • social_investment_analytical_layer (SIAL)
  • social_investment_data_foundation (SIDF)
  • SIAtoolbox
  • mha_data_definition

• Once the repository is downloaded and put into your IDI project folder, run the social_investment_analytical_layer scripts so that the all the SIAL tables are available for use in your database schema. We strongly recommended using the version in the submodule. Note when you create the SIAL tables the scripts will attempt to access to the following schemas in IDI_Clean (or the archives if you wish to use an older IDI refresh).

  • acc_clean
  • cor_clean
  • cyf_clean
  • data
  • dia_clean
  • hnz_clean
  • moe_clean
  • moh_clean
  • moj_clean
  • msd_clean
  • pol_clean
  • security

• If there are specific schemas listed above that you don't have access to, the SIAL main script (after it finishes running) will give you a detailed report on which SIAL tables were not created and why.

• Ensure that you have installed the SIAtoolbox library in R. Note that SIAtoolbox is not on the CRAN repository, and can be retrieved only from Github. Place this code in a folder in your IDI project, make sure you have the devtools package installed and loaded in R and then run devtools::install("/path/to/file/SIAtoolbox") in R.

• You can use the social_investment_data_foundation (SIDF) to roll up only those SIAL tables that were created. All these macros are made available via a sasautos call to the relevant lib folder. If you attempt to run the data foundation to create a variable from SIAL table that does not exist in your schema, the variables won't be created. You won't get an error, the variable just won't exist.

Folder descriptions

This folder contains all the code necessary to build characteristics and service metrics. Code is also given to create an outcome variable (highest qualification) for use and as an example of how the more complex variables are created and added to the main dataset.

include: This folder contains generic formatting scripts. lib: This folder is used to refer to reusable code that belongs to other repositories sasautos: This folder contains SAS macros. All scripts in here will be loaded into the SAS environment during the running of setup in the main script. sasprogs: This folder contains SAS programs. The main script that builds the dataset is located in here as well as the control file that needs to be populated with parameters for your analysis. sql: This folder contains sql scripts to query the database.

Instructions to run the vulnerable mothers project

Step A: Create population

1. Start a new SAS session
2. Open sasprogs/si_control_general.sas. Go to the yellow datalines and update any of the parameters that need changing. The one that is most likely to change if you are outside the SIA is the si_proj_schema. If you have made changes save and close the file
3. Open sasprogs/si_control_mother.sas. Go to the yellow datalines and update any of the parameters that need changing. These parameters are likely to change if you wish to extend the vulnerable mothers analysis to include additional variables. If you have made changes save and close the file.
4. Repeat step 3 for the sasprogs/si_control_child.sas control file.
5. Open sasprogs/si_main.sas and change the ``si_source_pathvariable to your project folder directory. Once this is done, run thesi_main.sas` script, which will build the datasets that are needed to do the analysis.

Step B: Data Preparation & Analysis

1. Open up rprogs/main.R This script controls all pieces of work done in R. The first part of the script sets the directories and loads the libraries. If you are doing work outside the SIA change the working directory to your project folder.
2. Confirm that you have all the packages installed in the load library section. Run the main.R script to create the analysis outputs. Note that the scripts perform

Step C: Identifying vulnerable groups and their characteristics

1. Check the output folder for outputs from the cluster analysis. A lasso regression is performed to identify a set of variables that strongly characterise each cluster. Clusters that exhibit high coefficients for risk factors can be thought of as at-risk groups.

Getting Help

If you have any questions email info@sia.govt.nz. Tracking number: SIA-2017-0261