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SMT_dental_report_region_ICB_level.rmd
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SMT_dental_report_region_ICB_level.rmd
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---
title: "Dental Data Pack"
subtitle: "`r params$region`"
date: "`r paste(format(Sys.time(), '%B, %Y'), ' - Reporting up to end of', format(Sys.Date() - lubridate::weeks(4), '%B, %Y'))`"
output:
xaringan::moon_reader:
css: [theme.css, nhsr-fonts.css]
self_contained: true
lib_dir: libs
nature:
ratio: '16:9'
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
params:
region: "South West"
---
```{r setup, include=FALSE}
library(tidyverse)
library(readxl)
library(DBI)
library(odbc)
library(reactable)
library(downloadthis)
library(lubridate)
library(knitr)
library(ggrepel)
library(xaringanExtra)
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
options(htmltools.dir.version = FALSE, htmltools.preserve.raw = FALSE)
options(knitr.duplicate.label = 'allow')
#avoid standard form
options(scipen=999)
# Suppress summarise info
options(dplyr.summarise.inform = FALSE)
#source(knitr::purl("SQLpulls.Rmd", output = tempfile()), local = TRUE)
#source(knitr::purl("Data_Processing.Rmd", output = tempfile()))
#source(knitr::purl("plotting.Rmd", output = tempfile()))
#source(knitr::purl("appendix_functions.Rmd", output = tempfile()))
```
```{r set ICB name, include=FALSE}
#get STPs for each region
if(params$region == "South West"){
region <- "South West"
STP1 <- "Bristol, North Somerset And South Gloucestershire ICB"
STP2 <- "Dorset ICB"
STP3 <- "Bath And North East Somerset, Swindon And Wiltshire ICB"
STP4 <- "Somerset ICB"
STP5 <- "Devon ICB"
STP6 <- "Cornwall And The Isles Of Scilly ICB"
STP7 <- "Gloucestershire ICB"
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3, STP4, STP5, STP6, STP7)
eval4 <- TRUE
eval5 <- TRUE
eval6 <- TRUE
eval7 <- TRUE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}else if(params$region == "South East"){
region <- "South East"
STP1 <- "Sussex ICB"
STP2 <- "Buckinghamshire, Oxfordshire And Berkshire West ICB"
STP3 <- "Surrey Heartlands ICB"
STP4 <- "Kent And Medway ICB"
STP5 <- "Hampshire And Isle Of Wight ICB"
STP6 <- "Frimley ICB"
STP7 <- NULL
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3, STP4, STP5, STP6)
eval4 <- TRUE
eval5 <- TRUE
eval6 <- TRUE
eval7 <- FALSE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}else if(params$region == "North West"){
region <- "North West"
STP1 <- "Greater Manchester ICB"
STP2 <- "Cheshire And Merseyside ICB"
STP3 <- "Lancashire And South Cumbria ICB"
STP4 <- NULL
STP5 <- NULL
STP6 <- NULL
STP7 <- NULL
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3)
eval4 <- FALSE
eval5 <- FALSE
eval6 <- FALSE
eval7 <- FALSE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}else if(params$region == "North East And Yorkshire"){
region <- "North East And Yorkshire"
STP1 <- "Humber And North Yorkshire ICB"
STP2 <- "South Yorkshire ICB"
STP3 <- "North East And North Cumbria ICB"
STP4 <- "West Yorkshire ICB"
STP5 <- NULL
STP6 <- NULL
STP7 <- NULL
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3, STP4)
eval4 <- TRUE
eval5 <- FALSE
eval6 <- FALSE
eval7 <- FALSE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}else if(params$region == "London"){
region <- "London"
STP1 <- "North Central London ICB"
STP2 <- "South East London ICB"
STP3 <- "North East London ICB"
STP4 <- "South West London ICB"
STP5 <- "North West London ICB"
STP6 <- NULL
STP7 <- NULL
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3, STP4, STP5)
eval4 <- TRUE
eval5 <- TRUE
eval6 <- FALSE
eval7 <- FALSE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}else if(params$region == "Midlands"){
region <- "Midlands"
STP1 <- "Leicester, Leicestershire And Rutland ICB"
STP2 <- "Herefordshire And Worcestershire ICB"
STP3 <- "Nottingham And Nottinghamshire ICB"
STP4 <- "Black Country ICB"
STP5 <- "Staffordshire And Stoke-On-Trent ICB"
STP6 <- "Northamptonshire ICB"
STP7 <- "Shropshire, Telford And Wrekin ICB"
STP8 <- "Lincolnshire ICB"
STP9 <- "Birmingham And Solihull ICB"
STP10 <- "Coventry And Warwickshire ICB"
STP11 <- "Derby And Derbyshire ICB"
STPs <- c(STP1, STP2, STP3, STP4, STP5, STP6, STP7, STP8, STP9, STP10, STP11)
eval4 <- TRUE
eval5 <- TRUE
eval6 <- TRUE
eval7 <- TRUE
eval8 <- TRUE
eval9 <- TRUE
eval10 <- TRUE
eval11 <- TRUE
}else if(params$region == "East Of England"){
region <- "East Of England"
STP1 <- "Hertfordshire And West Essex ICB"
STP2 <- "Mid And South Essex ICB"
STP3 <- "Norfolk And Waveney ICB"
STP4 <- "Cambridgeshire And Peterborough ICB"
STP5 <- "Bedfordshire, Luton And Milton Keynes ICB"
STP6 <- "Suffolk And North East Essex ICB"
STP7 <- NULL
STP8 <- NULL
STP9 <- NULL
STP10 <- NULL
STP11 <- NULL
STPs <- c(STP1, STP2, STP3, STP4, STP5, STP6)
eval4 <- TRUE
eval5 <- TRUE
eval6 <- TRUE
eval7 <- FALSE
eval8 <- FALSE
eval9 <- FALSE
eval10 <- FALSE
eval11 <- FALSE
}
```
```{css, echo=F}
/* Table width = 100% max-width */
.remark-slide table{
width: 100%;
}
/* Change the background color to white for shaded rows (even rows) */
.remark-slide thead, .remark-slide tr:nth-child(2n) {
background-color: white;
}
```
#### Introduction
This slide pack has been streamlined to help users to easily access the most important information. This change took effect as of June 2024 data. As part of this change we have also sought to make the slides more useful by making the following changes:
- Swapping from scheduled data (the month the dentist claimed for payment following a finished course of treatment) to calendar month data (the month the course of treatment finished). This change better reflects the activity of dentists.
- Including only GDS/PDS/PDS+ contracts where total contracted UDAs \>100. This means contracts with low UDA targets are excluded because they are usually not monitored against UDA delivery.
- The data is available in an Excel file available alongside this pack.
This pack will continue to evolve and change, any questions/comments should be directed to [england.primarycare.phodsdata@nhs.net](mailto:england.primarycare.phodsdata@nhs.net)
---
name: contents
#### Contents
- [Units of Dental Activity (UDA) - Calendar activity data standardised by working days](#uda-activity)
- [Units of Dental Activity (UDA) - Calendar year to date delivery](#ytd-uda-delivery)
- [Units of Dental Activity (UDA) - Monthly activity compared to previous financial year](#monthly-uda-activity)
- [Banded courses of treatment standardised by working days (including Foundation Dentists (FD))](#banded-CoT)
- [Banded Courses of treatment as % of February 2020 delivery (including FD)](#banded-CoT-Feb2020)
- [Percentage of total Courses of Treatment (CoTs) delivered which had DCP assistance](#DCP-CoT)
- [Percentage of total UDAs delivered which had DCP assistance by band](#DCP-UDA)
- [Unique dental patients seen](#unique-patients)
- [Unique dental patients seen as a percentage of February 2020 figures](#unique-patients-Feb2020)
- [Patients seen under the New Patient Premium definition](#NPP)
- [Patients seen under the New Patient Premium definition by band](#NPP-band)
- [Eligible contracts that have seen new patients](#NPP-contracts)
- [Oral health risk assessment](#BPE-contracts)
- [Units of Orthodontic Activity (UOA) - Calendar activity data standardised by working days](#UOA)
- [Units of Orthodontic Activity (UOA) - Calendar year to date delivery](#YTD-UOA-delivery)
- [Units of Orthodontic Activity (UOA) - Started/Completed](#YTD-UOA-STARTED)
---
name: uda-activity
#### UDA - Calendar activity data standardised by working days
```{r xaringan-panelset-UDA_delivery_wd, echo=FALSE}
xaringanExtra::use_panelset()
```
<font size="2">
.panelset[
.panel[.panel-name[`r region`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "Regional",
region_STP_name = region)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP1`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP1)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP2`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP2)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP3`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP3)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP4`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval4}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP4)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP5`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval5}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP5)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP6`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval6}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP6)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP7`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval7}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP7)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP8`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval8}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP8)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP9`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval9}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP9)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP10`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval10}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP10)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
.panel[.panel-name[`r STP11`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval = eval11}
plot_historic_UDA_delivery_wd(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP11)
```
- These graphs show the percentage of UDAs delivered compared to the expected delivery. Expected monthly delivery is standardised by working days in the month and calculated as Target UDAs\* Monthly working days/working days per year.
- For the current financial year, monthly performance is shown. For the previous financial years, annual average performance is shown.
- Over the course of the financial year this graph allows us to see patterns of delivery.
]
]
---
name: ytd-uda-delivery
#### UDA - Calendar year to date delivery
```{r xaringan-panelset-YTD_UDA_delivery, echo=FALSE}
xaringanExtra::use_panelset()
```
<font size="2">
.panelset[
.panel[.panel-name[`r region`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "Regional",
region_STP_name = region,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP1`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP1,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP2`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP2,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP3`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP3,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP4`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval4}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP4,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP5`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval5}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP5,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP6`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval6}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP6,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP7`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval7}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP7,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP8`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval8}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP8,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP9`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval9}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP9,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP10`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval10}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP10,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
.panel[.panel-name[`r STP11`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval11}
plot_YTD_UDA_UOA_delivery(data = UDA_calendar_data,
UDAorUOA = "UDA",
level = "STP",
region_STP_name = STP11,
plotChart = TRUE,
all_regions_and_STPs = FALSE)
```
- This graph shows the progress towards delivering against contracted UDAs in the financial year. Here, expected delivery assumes delivery is equal across all months.
]
]
---
name: monthly-uda-activity
#### UDA - Monthly activity compared to previous financial year
```{r xaringan-panelset-UDA_COM, echo=FALSE}
xaringanExtra::use_panelset()
```
<font size="2">
.panelset[
.panel[.panel-name[`r region`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "Regional",
region_STP_name = region)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP1`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP1)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP2`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP2)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP3`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP3)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP4`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval4}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP4)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP5`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval5}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP5)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP6`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval6}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP6)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP7`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval7}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP7)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP8`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval8}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP8)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP9`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval9}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP9)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP10`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval10}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP10)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
.panel[.panel-name[`r STP11`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval11}
plot <- plot_monthly_uda_delivered(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP11)
table <- table_working_days()
grid.arrange(plot, table, nrow = 1, widths = c(1, 1, 1),
layout_matrix = rbind(c(1, 1, 2)))
```
- This graph shows the number of UDAs delivered each month for the current financial year compared with the previous financial year.
- Some of the difference between months may be due to differences in the number of working days. The table shows working days by month for 2023/24 and 2024/25.
- As dentists have 62 days to claim for their activity, the most recent two months data are incomplete. We expect final data to be higher. Figures for the latest complete month of data are presented on this graph.
]
]
---
name: banded-CoT
#### Banded courses of treatment standardised by working days (including Foundation Dentists (FD))
```{r xaringan-panelset-banded_CoT, echo=FALSE}
xaringanExtra::use_panelset()
```
<font size="2">
.panelset[
.panel[.panel-name[`r region`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_banded_CoT(data = UDA_calendar_data_FD,
level = "Regional",
region_STP_name = region,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP1`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_banded_CoT(data = UDA_calendar_data_FD,
level = "STP",
region_STP_name = STP1,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP2`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP2,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP3`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center'}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP3,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP4`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval4}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP4,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP5`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval5}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP5,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP6`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval6}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP6,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP7`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval7}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP7,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP8`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval8}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP8,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```
- This graph shows the number of completed Courses of Treatment (CoTs) by band over time. Data has been standardised to working days to remove variation due to month length. It is calculated as completed CoTs\*Monthly working days/working days per year.
- From November 2022 band 2 was split into 2a, 2b and 2c. More information can be viewed here: [Units of dental activity (UDA) changes to Band 2 treatments from Friday 25 November](https://www.nhsbsa.nhs.uk/units-dental-activity-uda-changes-band-2-treatments-friday-25-november)
]
.panel[.panel-name[`r STP9`]
```{r echo = FALSE, warning = FALSE, message = FALSE, fig.width=14, fig.height=5, fig.align='center', eval=eval9}
plot_banded_CoT(data = UDA_calendar_data,
level = "STP",
region_STP_name = STP9,
plotChart = TRUE,
all_regions_and_STPs = FALSE,
asIndex = FALSE)
```