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R pipeline to clean and process Hospital Episode Statistics (HES) data
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README.md

README.md

HES pipeline

Open-source R pipeline to clean and process patient-level Hospital Episode Statistics (HES) & ONS Civil Registrations data, with the aim to produce analysis-ready datasets for a defined programme of analyses.

Project Status: in progress

Project Description

Hospital Episode Statistics (HES) is a database containing details of all hosptial admissions, A&E attendances and outpatient appointments at NHS hospitals in England.

Before it can be used for analysis, HES data requires cleaning (e.g. duplicate removal) and quality control as well as additional derived variables and tables. The complexity of HES. the large number of variables and the size of the data sets can makes this a challenging task.

This cleaning and processing workflow will be designed to ensure that the HES data is processed consistently and reproducably, and that every one of our pre-approved analysis projects work with the cleaned data sets.

Data Source

We are planning to use HES data linked to Civil Registrations (deaths) covering the last 10 years as well as quarterly data updated for the next three years. Our data application has been approved by the NHS Digital Data Access Request Service (DARS) and development of this pipeline has now commenced.

The data will be accessed in The Health Foundation's Secure Data Environment; a secure data analysis facility (accredited with the ISO27001 information security standard, and recognised for the NHS Digital Data Security and Protection Toolkit). No information that could directly identify a patient or other individual will be used.

Documentation (work in progress)

The doc folder contains information on

  • the protocol for HES data cleaning and processing used [to be added]
  • the design of the pipeline: ADR and process.md
  • the logs that are created
  • definitions of derived variables - to be updated
  • definitions of derived tables

Sections at the end of the README describe

  • how to use the pipeline
  • how to query the resulting SQLite database.

Pipeline design and features

The process file (process.md) describes the overall design of the pipeline, lists the necessary inputs and a high-level description of the steps in the workflow.

The flowchart shows how user input and data move through the different pipeline functions.

The pipeline

  • creates a SQLite connection and database
  • reads ONS and bridge files, merges them and adds them to the database
  • reads HES raw data files in chunks and adds it to the databasea after
    • checking if all expected columns are present
    • coercing data types (optional)
    • cleaning variables
    • deriving variables (based on a single row of data)
    • combining with public data on deprivation and CCGs (optional)
    • flags comorbidities (to be made optional)
  • flags duplicates in the database (optional)
  • creates inpatient spells (not yet implemented)
  • creates continuous inpatient spells (not yet implemented)
  • creates summary tables for the clean dataset

Architecture design record (ADR)

The architecture design record (ADR) captures architectural decision and design choices, along with their context, rationale and consequences.

So far, we have recorded decisions regarding

  • where and how the raw data is stored and, if necessary, updated (data_location.md and data_storage_and_access.md)
  • how the data is read in in chunks and how to determine the number of required chunks per file (chunking_large_data_file_optimisation.md)
  • how dates will be stored in the SQLite database (storing_dates_in_database.md)
  • the chosen method to compare A&E arrivel time of two records while identify duplicate records (comparing_arrivaltime_across_days.md)
  • hardcoding variable names during cleaning and deriving steps (to be added)
  • the methodology used to create inpatient spells (to be added)
  • the methodology used to create continuous inpatient spells (to be added)

Derived variables

Derived variables are documented in derived variables

How does it work?

As the data prepared in this pipeline is not publicly available, the code cannot be used to replicate the database. However, with modifications the code will be able to be used on other patient-level HES extracts to prepare the datasets for analysis. For more information on how the pipeline works please refer to the process document.

Requirements

The HES pipeline was built under R version 3.6.2 (2019-12-12) -- "Dark and Stormy Night".

The following R packages are required to run the HES pipeline:

  • data.table (1.12.2)
  • DBI (1.0.0)
  • dplyr (0.8.3)
  • furrr (0.1.0)
  • logger (0.1)
  • plyr (1.8.4)
  • rlang (0.4.0)
  • testthat (2.2.1)
  • tidylog (0.2.0)
  • tidyverse (1.2.1)
  • comorbidity (0.5.3)

Installation

Download the HES Pipeline by either downloading or cloning the repo.

Usage: running the pipeline to prepare HES for analysis

The pipeline can by run in two modes:

  1. Building a HES database from scratch. This is the default.
  2. Update mode (update = TRUE). This incorporates data updates into an existing HES database.

The following is needed to run the pipeline:

  • HES data extract The pipeline can process any of the following patient-level datasets: HES Admitted Patient Care, HES Accidents & Emergencies, HES Ouptatient care, HES Critical Care and ONS Mortality records (including the bridge file linking it to HES). It requires at least one of them. The raw data files have to be located in the same folder. The path will be provided as the data_path argument to the pipeline() function call.
  • SQLite database location (storage) This is where the SQLite database will be built. This location will need to have sufficient storage space available to accomodate the SQLite database, roughly equivalent to the combined file size of the raw HES data extract plus 2 x file size of the A&E data set (as the tables for inpatient spells and continuous inpatient spells will be added). The path to the folder will be provided as the database_path argument to the pipeline() function call.
  • Dataset codes The data_set_codes argument tells the pipeline which data sets to expect to find in the data_path folder. The codes only be all or a subset of "APC", "AE", "CC" and "OP", which also need to be present in the names of the raw files (in our experience, this is the case for raw HES files received directly from NHS Digital). ONS Mortality records are processed automatically if present in the data_path folder. The file names for mortality records and bridge files should contain "ONS" and "BF", respectively.
  • Optional: chunk sizes Each data file is read and processed in chunks, ie defnied a number of rows at a time. The default chunk size is 1 million lines but can also be set by the user. Larger chunk sizes (ie a lower number of chunks per file) decrease the overall processing time significanctly. We think that this is because for each chunk in a given file, fread() needs progressively longer to move to the right row number to start reading data. As each dataset can have a different number of variables, and therefore requires different amounts of memory per row, chunk sizes have to be defined for each dataset separately. It is recommended to test this on a smaller subset of the data first, as very large chunk sizes can lead RStudio to crash.
  • Expected column names The pipeline requires a csv file listing the expected column names for each data set. This table is expected to have at least two column, colnames and dataset, similar to [csv]. Column headers are automatically capitalised while the data is read in, so the column names in the csv file should be capitalised as well. This information will be used to check whether each raw data file contains all expected columns.
  • Optional: expected data types By default, the function used to read in the data will automatically detect column types. The pipeline providesoption to coere each variables into user-defined types by setting the coerce argument to TRUE. This required a third column type in the csv file mentioned above, see to [csv]. Note that since SQLite does not have a date datatype. Date variables need to be stored as integers. They should therefore be be listed as integers in the csv file.
  • Optional: merging reference data Additional reference data that can be merged in on the record level currenlty include Index of Multiple Deprivation (IMD), 2015 and/or 2019 versions, and CCG identifiers. The files paths to the reference files should be supplied as IMD_2014_csv, IMD_2019_csv and CCG_XLSX arguments and will be joined on patient LSOA11. The data files can be downloaded from [to be added].
  • Optional: flagging duplicate records This can be switched on by setting duplicate = TRUE. Warning: this will significantly increase the run time of the pipeline.

Currently the pipeline is designed to run in an RStudio session. From the R console compile the code:

> source("pipeline.R")

Then call pipeline(), providing as arguments a path to the data directory, a path to a directory for an SQLite database, a vector of dataset codes, a path to a csv with expected columns, inlcuding dataset codes and data types, an optional vector of the number of rows to be read at a time per datasets, and, if required,and a boolean to enable coercion. The data will be processed and written to the database. N.B. This is a slow process and takes up a fair amount of memory to run.

Example run:

> pipeline(data_path = "/home/user/raw-data/", database_path = "/home/user/database-dir/", data_set_codes = c("APC", "AE", "CC", "OP"), chunk_sizes = c(2000000, 5000000, 2000000, 3000000), expected_headers_file = "/home/user/expected_columns.csv", IMD_15_csv = "IMD_2015_LSOA.csv", IMD_19_csv = "IMD_2019_LSOA.csv", CCG_xlsx = "xchanges-to-ccg-dco-stp-mappings-over-time.xlsx", coerce = TRUE, update = FALSE, duplicate = FALSE)

Querying the clean HES data

library(tidyverse)
library(dbplyr)
library (DBI)

con <- dbConnect(RSQLite::SQLite(), paste0(database_path, "HES_db.sqlite"))

# List available tables
dbListTables(con)

# List available variables in the A&E table
dbListFields(con, "AE")

# Option 1: Query using dbplyr
# Select table
AE <- tbl(con, 'AE')

# Look at the first 5 rows
AE %>% 
  head() %>% 
  collect()

# Option 2: Query using SQL
dbGetQuery(con,'SELECT * FROM AE LIMIT 5')

dbDisconnect(con)

What NOT to do (to be added).

License

This project is licensed under the MIT License.

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