ae_attendances dataset

Tom Jemmett, The Strategy Unit

15/08/2019

This vignette explains how to use the ae_attendances dataset in R, and also details where it comes from and how it is generated.

The data is sourced from NHS England Statistical Work Areas and is available under the Open Government Licence v3.0.

The data contains all reported A&E attendances for the period April 2016 through March 2019

The dataset contains:

• period: the month that this activity relates to, stored as a date (1st of each month)
• org_code: the ODS code for the organisation that this activity relates to
• type: the Department Type for this activity, either 1, 2, or other
• attendances: the number of attendances for this department type at this organisation for this month
• breaches: the number of attendances that breached the 4 hour target
• admissions: the number of attendances that resulted in an admission to the hospital

First let’s load some packages and the dataset and show the first 10 rows of data.

library(knitr)
library(scales)
library(ggrepel)
library(lubridate)
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:dplyr':
#> 
#>     intersect, setdiff, union
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union
library(dplyr)
library(forcats)
library(tidyr)
#> 
#> Attaching package: 'tidyr'
#> The following objects are masked from 'package:Matrix':
#> 
#>     expand, pack, unpack

library(NHSRdatasets)

data("ae_attendances")

# format for display
ae_attendances %>%
  # set the period column to show in Month-Year format
  mutate_at(vars(period), format, "%b-%y") %>% 
  # set the numeric columns to have a comma at the 1000's place
  mutate_at(vars(attendances, breaches, admissions), comma) %>%
  # show the first 10 rows
  head(10) %>%
  # format as a table
  kable()

We can calculate the 4 hours performance for England as a whole like so:

england_performance <- ae_attendances %>%
  group_by(period) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  mutate(performance = 1 - breaches / attendances)

# format for display
england_performance %>% 
  # same format options as above
  mutate_at(vars(period), format, "%b-%y") %>% 
  mutate_at(vars(attendances, breaches), comma) %>%
  # this time show the performance column as a percentage
  mutate_at(vars(performance), percent) %>%
  # show the first 10 rows and format as a table
  head(10) %>%
  kable()

We can now plot the monthly performance

ggplot(england_performance, aes(period, performance)) +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = percent) +
  labs(x = "Month of attendance",
       y = "% of attendances that met the 4 hour standard",
       title = "NHS England A&E 4 Hour Performance",
       caption = "Source: NHS England Statistical Work Areas (OGL v3.0)")

We can clearly see the “Winter Pressures” where performance drops.

We can also inspect performance for the different types of department:

ae_attendances %>%
  group_by(period, type) %>%
  summarise_if(is.numeric, sum) %>%
  mutate(performance = 1 - breaches / attendances) %>%
  ggplot(aes(period, performance, colour = type)) +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = percent) +
  #facet_wrap(vars(type), nrow = 1) +
  theme(legend.position = "bottom") +
  labs(x = "Month of attendance",
       y = "% of attendances that met the 4 hour standard",
       title = "NHS England A&E 4 Hour Performance",
       subtitle = "By Department Type",
       caption = "Source: NHS England Statistical Work Areas (OGL v3.0)")

From this it appears as if only the type 1 departments have the seasonal drops, type 2 and “other” departments remain pretty consistent.

performance_by_trust <- ae_attendances %>%
  group_by(org_code, period) %>%
  # make sure that this trust has a type 1 department
  filter(any(type == 1)) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  mutate(performance = 1 - breaches / attendances)

# format for display
performance_by_trust %>%
  mutate_at(vars(period), format, "%b-%y") %>% 
  mutate_at(vars(attendances, breaches), comma) %>%
  mutate_at(vars(performance), percent) %>%
  head(10) %>%
  kable()

performance_by_trust_ranking <- performance_by_trust %>%
  summarise(performance = 1 - sum(breaches) / sum(attendances)) %>%
  arrange(performance) %>%
  pull(org_code) %>%
  as.character()

print("Bottom 5")
#> [1] "Bottom 5"
head(performance_by_trust_ranking, 5)
#> [1] "RQW" "RWD" "RXW" "RX1" "RHU"

print("Top 5")
#> [1] "Top 5"
tail(performance_by_trust_ranking, 5)
#> [1] "RA4" "RBD" "RVW" "RCU" "RC9"

performance_by_trust %>%
  ungroup() %>%
  mutate_at(vars(org_code), fct_relevel, performance_by_trust_ranking) %>%
  filter(org_code %in% c(head(performance_by_trust_ranking, 5),
                         tail(performance_by_trust_ranking, 5))) %>%
  ggplot(aes(period, performance)) +
  geom_line() +
  geom_point() +
  scale_y_continuous(labels = percent) +
  facet_wrap(vars(org_code), nrow = 2) +
  theme(legend.position = "bottom") +
  labs(x = "Month of attendance",
       y = "% of attendances that met the 4 hour standard",
       title = "NHS England A&E 4 Hour Performance",
       subtitle = "Bottom 5/Top 5 over the whole 3 years",
       caption = "Source: NHS England Statistical Work Areas (OGL v3.0)")

ae_attendances %>%
  filter(period == last(period)) %>%
  group_by(org_code) %>%
  filter(any(type == 1)) %>%
  summarise_at(vars(attendances, breaches), sum) %>%
  mutate(performance = 1 - breaches/attendances,
         overall_performance = 1 - sum(breaches)/sum(attendances),
         org_code = fct_reorder(org_code, -performance)) %>%
  #
  arrange(performance) %>%
  # lets highlight the organsiations that are at the lower and upper quartile
  # and at the median. First "tile" the data into 4 groups, then we use the
  # lag function to check to see if the value changes between rows. We will get
  # NA for the first row, so replace this with FALSE
  mutate(highlight = ntile(n = 4),
         highlight = replace_na(highlight != lag(highlight), FALSE)) %>%

  ggplot(aes(org_code, performance)) +
  geom_hline(aes(yintercept = overall_performance)) +
  geom_point(aes(fill = highlight), show.legend = FALSE, pch = 21) +
  geom_text_repel(aes(label = ifelse(highlight, as.character(org_code), NA)),
                  na.rm = TRUE) +
  scale_fill_manual(values = c("TRUE" = "black",
                               "FALSE" = NA)) +
  scale_y_continuous(labels = percent) +
  theme_minimal() +
  theme(panel.grid = element_blank(),
        axis.text.x = element_blank(),
        axis.line = element_line(),
        axis.ticks.y = element_line())