Patients Discharged from Psychiatric Hospitals in Scotland, 2002-2020

SIMD
mental health
inequality
deprivation
Summary of differences in discharges from psychiatric hospitals in Scotland, comparing people from least and most deprived areas
Author

Chantel Davies

Published

December 12, 2021

Overview

Link to dashboard on GitHub

The aim of this dashboard project is to summarise the impact of deprivation on psychiatric patient hospitalizations across Scotland, using discharge data from the Scottish Public Health Observatory for the most recent period for which data are available: 2017-18 to 2019-20. The main outputs are a map and summary statistics to compare between the most deprived and least deprived areas across each of Scotland’s local authorities and health boards.

To produce the map, I used data of psychiatric patient hospitalization discharges from each council area in Scotland, including quintile data from the Scottish Index of Multiple Deprivation. These data can be downloaded from the Scottish Public Health Observatory 2021. Also necessary is a quality shapefile of Scotland, coded to include geometry linked to council area, available from https://www.spatialdata.gov.scot/geonetwork/srv/eng/catalog.search#/home.

About the data

Definition: Patients discharged from psychiatric hospitals: 3-year rolling average number and directly age-sex standardised rate per 100,000 population. All rates have been standardised against the European standard population(ESP2013) and 2011-base population estimates. Patient is selected only once per year, based on their discharge date. Values less than 10 are suppressed. No rounding. All age-groups and sexes.

Data Source: Public Health Scotland (SMR04)

Geography code: A standard code assigned to geographical areas, which in this case is council area

Numerator: Number of patients discharged from psychiatric hospitals; denominator: total population

Indicator-measure: Age-sex standardised rate per 100,000

Quintile: Based on the Scottish Index of Multiple Deprivation (SIMD), five categories of deprivation are used from 1 - most deprived (the 20% most deprived) to 5 - least deprived (the 20% least deprived). All other variables are aligned to these data, where applicable, specifically, geography code, numerator, indicator, and confidence intervals

Period: financial year from 2002/03-2019/20, 3-year aggregates

Confidence interval: Dobson’s method

File structures and set-up

When working on building these types of projects, I follow a file structure recommended in this r-bloggers post by Martin Chan, and shown in the image below.

The folders and files are arranged in a hierarchy. The first level is 'Your Project Name' with an .Rproj (R Project File) and three folders: a 'Data' folder, containing all data files needed for the analysis; a 'Script' folder, containing a folder called 'Functions' for any functions created and used, and separate .r or .rmd files for each analysis; and an 'Output' folder with a folder each for 'Plots' and 'Data'

Diagram of R project file structure

Joining data as .csv files

The first step is to acquire the data from the Scottish Public Health Observatory as .csv files which are downloaded into pph_csv_files folders. I moved all separate files into a /data/pph_csv_files folder to keep them in one place. All time periods for all areas were initially chosen to ensure there would be sufficient data of an appropriate quality to be able to generate an interesting map.

  1. Create a collection of .csv files saved into the current directory, first by defining the working directory where the csv files are located.
mydir = "../../data/pph_csv_files/"
  1. Then specify the filepath and filetypes to search for, and include the full file names in the object
myfiles = list.files(path = mydir, pattern = "*.csv", full.names = TRUE)
  1. Inspect the object and check there are the correct number of files listed as the ones downloaded. You should also be able to see the number of files listed in the environment pane.
myfiles

The R Studio ‘Environment’ tab showing ‘myfiles’ with 32 files listed

  1. Load the plyr and readr packages. Use the ldply function from the plyr package and ’read_csv from the readr package to join all .csv files into one. The show_col_types = FALSE argument suppresses the output in the terminal.
library(plyr)
library(readr)

all_data = ldply(myfiles, readr::read_csv, show_col_types = FALSE)
  1. View the first five rows and columns 2-5 of the data.
all_data[1:5, 2:5]
  geography_code           quintile
1      S12000033  1 - most deprived
2      S12000033                  2
3      S12000033                  3
4      S12000033                  4
5      S12000033 5 - least deprived
                                                 period numerator
1 2002/03 to 2004/05 financial years; 3-year aggregates     212.7
2 2002/03 to 2004/05 financial years; 3-year aggregates     173.0
3 2002/03 to 2004/05 financial years; 3-year aggregates     169.3
4 2002/03 to 2004/05 financial years; 3-year aggregates      82.3
5 2002/03 to 2004/05 financial years; 3-year aggregates      76.7
  1. Create a tibble of the files.
all_data <- tibble::as_tibble(all_data)
  1. A sample of the dataset is shown here to demonstrate the main columns to be used in this project. Using package dplyr to first select the columns of interest, then filter the rows of interest - quintiles 1 and 5 - and slice the first 10 rows. Each of these commands can be ‘piped’ (%>% from the magrittr package) through to package kableExtra for producing a neatly formatted table.
library(kableExtra)
dplyr::select(all_data, c("geography_code",
                          "quintile",
                          "period",
                          "numerator",
                          "indicator_measure",
                          "lower_confidence_interval",
                          "upper_confidence_interval")) %>% 
  filter(quintile %in% c("1 - most deprived",
                         "5 - least deprived")) %>%
  slice(1:10) %>% 
  kableExtra::kbl() %>% 
  kable_material(c("striped", "hover"))
geography_code quintile period numerator indicator_measure lower_confidence_interval upper_confidence_interval
S12000033 1 - most deprived 2002/03 to 2004/05 financial years; 3-year aggregates 212.7 527.2 456.6 605.4
S12000033 5 - least deprived 2002/03 to 2004/05 financial years; 3-year aggregates 76.7 197.3 153.1 249.6
S12000033 1 - most deprived 2003/04 to 2005/06 financial years; 3-year aggregates 213.0 520.4 450.8 597.6
S12000033 5 - least deprived 2003/04 to 2005/06 financial years; 3-year aggregates 78.7 207.5 162.0 261.4
S12000033 1 - most deprived 2004/05 to 2006/07 financial years; 3-year aggregates 212.3 518.0 448.2 595.2
S12000033 5 - least deprived 2004/05 to 2006/07 financial years; 3-year aggregates 72.0 191.6 148.1 243.4
S12000033 1 - most deprived 2005/06 to 2007/08 financial years; 3-year aggregates 196.7 479.6 412.6 554.1
S12000033 5 - least deprived 2005/06 to 2007/08 financial years; 3-year aggregates 68.3 181.6 139.3 232.2
S12000033 1 - most deprived 2006/07 to 2008/09 financial years; 3-year aggregates 190.0 466.3 399.8 540.3
S12000033 5 - least deprived 2006/07 to 2008/09 financial years; 3-year aggregates 64.7 169.0 129.0 217.1
  1. Create a back up data file of all combined files that R can read, and save in the same directory as the individual .csv files.
save(all_data, file = "../../data/pph_csv_files/all_data.rda")
  1. Do the same in .csv format, using the readr package and write_excel_csv function.
readr::write_excel_csv(all_data, "../../data/pph_csv_files/all_data.csv")

Data checking and cleaning

Before thinking of analysis, I like to carry out various checks to make sure the data object(s) is (are) of the type I need and coded appropriately, for example, categories are factors, numeric and integer data are recognised as such. As these data are cleaned and released for public use, there is very little to do here.

Standard checks I do:

  1. Check the type of object to make sure data are in the correct format. In this case, tibble and dataframe.
class(all_data)
[1] "tbl_df"     "tbl"        "data.frame"
  1. Look at the column names to make sure there are no errors and identify if any names need to be changed for clarity.
names(all_data)
 [1] "indicator"                              
 [2] "geography_code"                         
 [3] "quintile"                               
 [4] "period"                                 
 [5] "numerator"                              
 [6] "indicator_measure"                      
 [7] "lower_confidence_interval"              
 [8] "upper_confidence_interval"              
 [9] "definition"                             
[10] "relative_inequality_gap"                
[11] "lower_confidence_interval_relative_ineq"
[12] "upper_confidence_interval_relative_ineq"
[13] "absolute_inequality_gap"                
[14] "lower_confidence_interval_absolute_ineq"
[15] "upper_confidence_interval_absolute_ineq"
[16] "population_attributable_risk"           
  1. Check dimensions of the dataframe object. Here there should be 3072 rows, and 16 columns.
dim(all_data)
[1] 12288    16
  1. Check the data structure to see if variables are categorised correctly.
str(all_data)
tibble [12,288 × 16] (S3: tbl_df/tbl/data.frame)
 $ indicator                              : chr [1:12288] "Psychiatric patient hospitalisations" "Psychiatric patient hospitalisations" "Psychiatric patient hospitalisations" "Psychiatric patient hospitalisations" ...
 $ geography_code                         : chr [1:12288] "S12000033" "S12000033" "S12000033" "S12000033" ...
 $ quintile                               : chr [1:12288] "1 - most deprived" "2" "3" "4" ...
 $ period                                 : chr [1:12288] "2002/03 to 2004/05 financial years; 3-year aggregates" "2002/03 to 2004/05 financial years; 3-year aggregates" "2002/03 to 2004/05 financial years; 3-year aggregates" "2002/03 to 2004/05 financial years; 3-year aggregates" ...
 $ numerator                              : num [1:12288] 212.7 173 169.3 82.3 76.7 ...
 $ indicator_measure                      : num [1:12288] 527 416 425 221 197 ...
 $ lower_confidence_interval              : num [1:12288] 457 356 360 173 153 ...
 $ upper_confidence_interval              : num [1:12288] 605 484 498 278 250 ...
 $ definition                             : chr [1:12288] "Age-sex standardised rate per 100,000" "Age-sex standardised rate per 100,000" "Age-sex standardised rate per 100,000" "Age-sex standardised rate per 100,000" ...
 $ relative_inequality_gap                : num [1:12288] NA NA NA NA NA 60.8 NA NA NA NA ...
 $ lower_confidence_interval_relative_ineq: num [1:12288] NA NA NA NA NA 23.8 NA NA NA NA ...
 $ upper_confidence_interval_relative_ineq: num [1:12288] NA NA NA NA NA 97.8 NA NA NA NA ...
 $ absolute_inequality_gap                : num [1:12288] NA NA NA NA NA ...
 $ lower_confidence_interval_absolute_ineq: num [1:12288] NA NA NA NA NA ...
 $ upper_confidence_interval_absolute_ineq: num [1:12288] NA NA NA NA NA ...
 $ population_attributable_risk           : num [1:12288] NA NA NA NA NA 44.6 NA NA NA NA ...
  1. Generate summary statistics to check for e.g. potential anomalies, missing values, minimum and maximum values, relationship between median and mean.
summary(all_data)
  indicator         geography_code       quintile            period         
 Length:12288       Length:12288       Length:12288       Length:12288      
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
                                                                            
   numerator      indicator_measure lower_confidence_interval
 Min.   :  10.0   Min.   :  40.8    Min.   :  4.4            
 1st Qu.:  47.7   1st Qu.: 187.8    1st Qu.:134.9            
 Median :  86.3   Median : 272.1    Median :217.5            
 Mean   : 172.7   Mean   : 296.9    Mean   :235.9            
 3rd Qu.: 184.3   3rd Qu.: 367.2    3rd Qu.:306.8            
 Max.   :2552.7   Max.   :1119.2    Max.   :947.7            
 NA's   :716                                                 
 upper_confidence_interval  definition        relative_inequality_gap
 Min.   : 108.9            Length:12288       Min.   :-12.10         
 1st Qu.: 260.3            Class :character   1st Qu.: 53.50         
 Median : 340.2            Mode  :character   Median : 64.45         
 Mean   : 373.9                               Mean   : 62.74         
 3rd Qu.: 446.4                               3rd Qu.: 75.88         
 Max.   :1311.4                               Max.   :101.50         
                                              NA's   :10240          
 lower_confidence_interval_relative_ineq
 Min.   :-105.200                       
 1st Qu.:   3.375                       
 Median :  25.350                       
 Mean   :  21.721                       
 3rd Qu.:  44.525                       
 Max.   :  72.400                       
 NA's   :10240                          
 upper_confidence_interval_relative_ineq absolute_inequality_gap
 Min.   : 14.40                          Min.   : -62.9         
 1st Qu.: 83.17                          1st Qu.: 299.0         
 Median :102.40                          Median : 385.6         
 Mean   :103.77                          Mean   : 383.7         
 3rd Qu.:122.15                          3rd Qu.: 475.1         
 Max.   :212.20                          Max.   :1040.2         
 NA's   :10240                           NA's   :10240          
 lower_confidence_interval_absolute_ineq
 Min.   :-250.10                        
 1st Qu.:  18.12                        
 Median : 146.70                        
 Mean   : 156.08                        
 3rd Qu.: 267.20                        
 Max.   : 801.20                        
 NA's   :10240                          
 upper_confidence_interval_absolute_ineq population_attributable_risk
 Min.   :  49.1                          Min.   :-34.50              
 1st Qu.: 469.8                          1st Qu.: 32.75              
 Median : 601.0                          Median : 43.05              
 Mean   : 611.4                          Mean   : 41.11              
 3rd Qu.: 737.4                          3rd Qu.: 52.65              
 Max.   :1400.8                          Max.   : 70.20              
 NA's   :10240                           NA's   :10240               
  1. Format the text in the period column to show just the year ranges without the additional text, then replace the forward slash (/) with a hyphen (-). There are many ways to do this, but here I use the gsub function from base R suite of functions (I started using R before tidyverse and even R Studio were invented, and I still use some of these functions):
all_data$period <- gsub(" financial years; 3-year aggregates", "", all_data$period)

all_data$period <- gsub ("/", "-", all_data$period)
  1. Subset data to create a smaller dataframe of all rows, and columns 2 to 8 inclusive. Convert period and quintile columsn to factors.
all_data_subset <- all_data[, 2:8]

all_data_subset$period <- factor(all_data_subset$period)

all_data_subset$quintile <- factor(all_data_subset$quintile)
  1. Check for unique and missing values (output not shown here for brevity, but you can check these yourself).
all_data_subset$numerator %>% unique()
all_data_subset$numerator %>% table()

Observations on the data

  1. Overall the mean is greater than the median for numerator and indicator_measure variables, indicating the data are skewed to the right and may be influenced by high outlying values. The histogram below illustrates this concept.
  2. There are notably large differences between 3rd quartiles and max values, indicating presence of outlying values.
hist(all_data_subset$indicator_measure)

However these measures are based on the whole dataset all_data_subset, and have not been filtered by time period or geographical location. I need to dig deeper into the data to identify possible temporal or spatial trends that may be worth further investigation.

Data visualisations

Boxplots

The first set of visuals are boxplots. These types of plots are useful for checking the range and spread of a dataset as they show the summary statistics generated earlier in visual form. They are also useful for identifying distribution, skewness and outliers - all of which can influence the robustness and reliability of statistical models.

As I am interested in identifying variation in psychiatric patient hospitalisations attributable to deprivation, I will visualise a subset of data from quintiles 1 (most deprived) and 5 (least deprived), across all time periods.

I use the following steps:

  1. Call ggplot from package ggplot2 and create a subset of data from the quintiles.
  2. Specify the levels within the quintile column with quintile %in% c(“1 - most deprived”, “5 - least deprived”).
  3. Call the aesthetics to plot time period on the x axis and indicator_measure on the y axis. Boxplots are colour-coded by quintile (using the viridis palette).
  4. geom_boxplot(width = 0.5) alters the width of each boxplot to improve fit on the page.
  5. Labels are added with labs, where title, subtitle and caption can be specified.
  6. The viridis palette is used to fill the boxplots with colourblind safe colours.
  7. The two groups of boxplots are placed side by side using facet_wrap based on quintile order.
  8. Additional aesthetics are managed by the theme_bw and theme() functions.
  9. theme_bw creates a simple theme with limited formatting; it is important to use theme() for adjusting other elements of formatting as it has greater flexibility than theme_bw.
psych_boxplot <- ggplot(subset(all_data_subset, 
              quintile %in% c("1 - most deprived", "5 - least deprived")), 
       aes(x = period, y = indicator_measure, fill = quintile)) + 
  geom_boxplot(width = 0.5) +
  labs(title = "Comparison of Psychiatric Patient Hospitalisations from most deprived\nand least deprived areas of Scotland, 2002 to 2020",
       subtitle = "Age-standardised rate per 100,000",
       caption = "Source: Scottish Public Health Observatory 2021") +
  scale_fill_viridis_d(alpha = 0.5) +
  facet_wrap(quintile~.) +
  theme_bw() +
  theme(panel.background = element_blank(), # Removes heavy background formatting
        panel.border = element_blank(), # Removes the outline border around the plot
        strip.background = element_rect(fill = "white"), # Changes the background fill from the plot titles ("1 most deprived", "5 - least deprived") from gray to colour of choice
        strip.text.x = element_text(size = 12), # Changes text size of plot titles ("1 most deprived", "5 - least deprived")
        legend.position = "none", # Removes the legend
        plot.title = element_text(vjust = 1, lineheight = 1.15), # Moves the vertical alignment of the plot title and increases the spacing between the title lines
        plot.subtitle = element_text(size = 11, vjust = 1, lineheight = 1.5), # Modifies the features of the subtitle: text size, vertical adjustment and lineheight
        plot.caption = element_text(size = 10, vjust = -1, hjust = 0), # Modifies the caption attributes, including horizontal adjustment
        axis.title.x = element_blank(), # Removes the x axis title
        axis.title.y = element_blank(), # Removes the y axis title
        axis.text.x = element_text(size = 10, color = "black", angle = 90), # Modifies the x axis text
        axis.text.y = element_text(size = 10, color = "black")) # Modifies the y axis text

By calling the ggplot object, psych_boxplot, you can see your graphic nicely rendered:

Summary

For age-standardised rate per 100,000 population, there is a higher proportion of discharges from psychiatric hospitals for people from most deprived areas than least deprived areas. From 2002 to 2011 the boxplots of most deprived data show a greater spread of datapoints, with more extreme outliers, than the boxplots for least deprived areas. There has been an overall decline in the rate of hospitalisations since the first set of aggregated data were released covering 2003-04 to 2005-06 until the final period of 2017-18 to 2019-2020.

Barplot

Barplots are useful to compare differences between categories. Including error bars, for standard error, standard deviation or confidence interval (depending on your analysis), can be helpful in determining whether differences observed between categories may be significant or not. This is relatively easy to do with R.

I will follow these steps to produce a barchart with confidence interval bars for the most deprived and least deprived for each local authority in Scotland.

  1. Filter the quintiles of interest from the subsetted dataframe, all_data_subset.

  2. Set the aesthetics (known as aes in ggplot), using period for the x axis and indicator_measure for the y axis. The fill argument is used to define custom colours as used for the boxplots.

  3. The geom_bar function specifies a barchart, requiring additional arguments to create groups based on the two quintile categories and clustered according to time period. The width of the bar needs to be specified here, using the same width for the errorbars in order to ensure they are centred properly in the barchart.

  4. The errorbars are specified from the data - lower_confidence_interval and upper_confidence_interval and positioned correctly using the position = position_dodge argument.

  5. The barplots are separated into local authority based on geography_code - these are an important part of mapping and will be discussed further in the mapping section.

psych_barplot <- all_data_subset %>% 
  filter(quintile %in% c("1 - most deprived", "5 - least deprived")) %>% 
  ggplot(aes(x = period, y = indicator_measure, fill = quintile)) +
  geom_bar(stat = "identity",
           position = "dodge",
           width = 0.8) + # Specifying the bar width here will make it easier to centre error bars
  geom_errorbar(aes(ymin = lower_confidence_interval,
                    ymax = upper_confidence_interval),
                width = 0.4,
                position = position_dodge(width = 0.8)) + # Use the 'width' argument to centre error bars
  labs(title = "Comparison of Psychiatric Patient Hospitalisations from most deprived\nand least deprived areas of Scotland, 2002 to 2020",
       subtitle = "Age-standardised rate per 100,000",
       caption = "Source: Scottish Public Health Observatory 2021") +
  scale_fill_viridis_d(alpha = 0.5) +
  facet_wrap(.~geography_code, ncol = 4) +
  theme_bw() +
  theme(panel.background = element_blank(), # Removes heavy background formatting
        panel.border = element_blank(), # Removes the outline border around the plot
        strip.background = element_rect(fill = "white"), # Changes the background fill from the plot titles ("1 most deprived", "5 - least deprived") from gray to colour of choice
        strip.text.x = element_text(size = 10), # Changes text size of plot titles ("1 most deprived", "5 - least deprived")
        legend.position = "none", # Removes the legend
        plot.title = element_text(vjust = 1, lineheight = 1.15), # Moves the vertical alignment of the plot title and increases the spacing between the title lines
        plot.subtitle = element_text(size = 11, vjust = 1, lineheight = 1.5), # Modifies the features of the subtitle: text size, vertical adjustment and lineheight
        plot.caption = element_text(size = 10, vjust = -1, hjust = 0), # Modifies the caption attributes, including horizontal adjustment
        axis.title.x = element_blank(), # Removes the x axis title
        axis.title.y = element_blank(), # Removes the y axis title
        axis.text.x = element_text(size = 10, color = "black", angle = 90), # Modifies the x axis text
        axis.text.y = element_text(size = 10, color = "black")) # Modifies the y axis text

psych_barplot

By calling the ggplot object, psych_boxplot, you can see your graphic nicely rendered:

Summary

For age-standardised rate per 100,000 population, there is a higher proportion of discharges from psychiatric hospitals for people from most deprived areas than least deprived areas. There has been an overall decline in the rate of hospitalisations since the first set of aggregated data were released covering 2003-04 to 2005-06 until the final period of 2017-18 to 2019-2020.

As with the boxplot chart, the x-axis at the bottom of the chart shows the time series for each pair of bars. The faceting has separated the barplots by geography code, which has been used to label each plot. It is possible to see that some geographical areas have greater differences in the number of discharges between the most and least deprived areas, with confidence intervals that do not overlap; for a few areas the differences are not so great.

Map

To start making the map I downloaded a shapefile of Local Authority Boundaries - Scotland as a base.