In HorridTom/clahrcnwlhf: Heart Failure Care Bundle Project Analysis, CLAHRC NWL

This vignette covers the data cleaning process from the raw data provided by North West London Hospitals to a clean dataset ready for analysis.

Input

The raw data is contained in the file /data/admission_data.rda. This file contains a single dataframe, admission_data, which is created from the original raw data files in /data-raw by the script /data-raw/load-raw-data.R which simply stitches the separate files together.

The functions required for cleaning the data are all contained within the data_cleaning.R script.

Date formatting

All dates in the raw data are expressed as character strings starting yyyy-mm-dd. We therefore use the regex "^[0-9]{4}-[0-9]{2}-[0-9]{2}" to extract the date part of strings in the following columns of the raw data: AdmissionDate, DischargeDate, EpisodeStartDate, EpisodeEndDate.

Missing Data

As part of the data extract and pseudonymisation process, missing data has been represented as both "NULL" and "#N/A" in the raw data. The clean_data function resolves both of these to NA, resulting in the following distribution of missing data across columns.

admission_data_clean <- clahrcnwlhf::clean_data(clahrcnwlhf::admission_data,
                                                restrict_disch_date = FALSE)
library(knitr)
library(scales)
nac <- clahrcnwlhf::na_count(admission_data_clean)
n <- nrow(admission_data_clean)
nact <- data.frame(nac,nac/n*100)
colnames(nact) <- c("Number_of_NAs", "Percent_NAs")
knitr::kable(nact[order(nact[,2]),])

For some fields, it is not important that there is a significant amount of missing data; e.g. the fact that many of the secondary diagnosis fields are missing may be entirely natural - not all episodes of care involve nine different diagnoses!

To check for any patterns in missing data over time, let's look at this by year.

#admission_data_clean <- clahrcnwlhf::clean_data(clahrcnwlhf::admission_data,
#                                                restrict_disch_date = FALSE)
library(knitr)
library(scales)
md <- clahrcnwlhf::missing_data_table(admission_data_clean)
knitr::kable(t(md))

The percentage of missing data for the following fields looks higher for 2016 compared with previous years: CSPAdmissionTime, CSPDischargeTime, CSPLastWard, PrimaryDiagnosis. Let's look at these percentages at a more granular timescale.

#admission_data_clean <- clahrcnwlhf::clean_data(clahrcnwlhf::admission_data,
#                                                restrict_disch_date = FALSE)
p <- clahrcnwlhf::missing_data_table(admission_data_clean, split_by = '%Y-%m',
                                     result = 'percent_no_total')
pmd_monthly <- p[,colnames(p) %in% c("CSPAdmissionTime","CSPDischargeTime",
                                     "PrimaryDiagnosis","CSPLastWard")]
matplot(pmd_monthly, type = c("b"),pch=1:4,col = 1:4, xaxt="n")
axis(side=1,at=1:nrow(pmd_monthly),labels=rownames(pmd_monthly), las = 2, cex.axis =0.6)
legend("topleft", legend = colnames(pmd_monthly), col=1:4, pch=1:4)

This shows two things:

1. The proportion of missing data in the PrimaryDiagnosis field goes up dramatically for episodes with DischargeDate in the last two months of data (October and November 2016)
2. The proportions of missing data in the fields CSPAdmissionTime, CSPDischargeTime, CSPLastWard, go up by an order of magnitude for episodes with DischargeDate in the last seven months of data (May - November 2016).

The increase in missing PrimaryDiagnosis values in the last two months (1) is likely to be a result of the usual delay in coding. Therefore these months will not be used in the analysis.

The missing data in the fields CSPAdmissionTime, CSPDischargeTime, CSPLastWard is not so easily explained, and Steve Hiles from North West London Hospitals is investigating this at present (25th November 2016).

For now, let's look at the new missing data summary excluding those last two months:

#admission_data_clean <- clahrcnwlhf::clean_data(clahrcnwlhf::admission_data,
#                                                restrict_disch_date = TRUE)
library(knitr)
library(scales)
nac <- clahrcnwlhf::na_count(admission_data_clean)
n <- nrow(admission_data_clean)
nact <- data.frame(nac,nac/n*100)
colnames(nact) <- c("Number_of_NAs", "Percent_NAs")
knitr::kable(nact[order(nact[,2]),])

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HorridTom/clahrcnwlhf documentation built on May 7, 2019, 4:02 a.m.