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---
title: "`r config$publication_name`"
pagetitle: PfD England summary
always_allow_html: true
output:
word_document:
toc: yes
toc_depth: '3'
html_document:
anchor_sections: no
css:
- www/style.css
- www/nhs-min.css
toc: yes
toc_depth: 3
toc_float:
collapsed: no
---
<html lang="en">
```{r setup, include=FALSE}
# set code chunk options to disable echo by default
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
message = FALSE)
```
<div class = "quartoheader", id = "fixed-header">
<header class="nhsuk-header" role="banner">
<div class="nhsuk-width-container nhsuk-header__container">
<div class="nhsuk-header__logo nhsuk-header__logo--only">
<a class="nhsuk-header__link" href="https://www.nhsbsa.nhs.uk/" aria-label="NHSBSA home" target="_blank">
<img class="nhsuk-logo" src="www/logo-nhsbsa.svg"" name="NHSBSA logo" alt="NHS Business Services Authority">
</a>
</div>
</div>
</div>
<!-- JS to handle hiding the header on scroll -->
<script>
document.addEventListener("scroll", function() {
var header = document.getElementById("fixed-header");
var toc = document.getElementById("TOC");
var BackTopButton = document.getElementById("backtop-button");
var scrollPosition = window.scrollY || document.documentElement.scrollTop;
if(scrollPosition > 88) {
header.style.top = "-200px";
toc.style.setProperty('margin-top', '24px', 'important');
BackTopButton.style.setProperty('visibility', 'visible', 'important');
} else
{
header.style.top = "0";
toc.style.setProperty('margin-top', '140px', 'important');
BackTopButton.style.setProperty('visibility', 'hidden', 'important');
}
}
);
</script>
<!-- JS to handle back to top button -->
<script>
function BackTop() {
document.body.scrollTop = 0;
document.documentElement.scrollTop = 0;
}
</script>
<button onclick="BackTop()" id="backtop-button" style="opacity: 0.7; pointer-events: initial; visibility: visible;"><span aria-hidden="true">▲</span>Back to top</button>
<main>
# `r config$publication_sub_name` {.toc-ignore}
`r paste0("Published ", config$publication_date)`
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-12}
`r infoBox_border(header = "Feedback", text = paste0("We are interested in any feedback about the publication, which you can send by using our ", htmltools::HTML(paste("<a href='",config$stats_survey_link,"'>Official Statistics feedback survey</a>")),"."), width = "100%")`
:::
::::
## Key findings
In 2023/24:
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>The cost of prescribed items for treating diabetes was £1.67 billion.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>3.6 million identified patients were prescribed a diabetes item.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>Areas of greater deprivation had the highest number of identified patients who were being prescribed drugs used in treating diabetes.</b>", width = "100%")`
:::
::::
There were 71 million items prescribed for treating diabetes, an increase of 21 million items since 2015/16.
The cost was £1.67 billion, which accounts for 15% of the total spend on all prescribed items. This compares to £960 million in 2015/16, which accounted for 10% of the total spending on all prescribed items.
Antidiabetic drugs were the most prescribed drugs used in treating diabetes in England with 53 million items at a cost of £960 million. The costs of antidiabetic drugs have increased by 127% since 2015/16 from £420 million.
There were 3.6 million identified patients that were prescribed items used in diabetes in England. This was a 7% increase from 3.4 million identified patients in 2022/23, and a 34% increase from 2.7 million in 2015/16.
The most common group to receive prescribing for items used in diabetes in 2023/24 was male patients aged 60 to 64 with 270,000 identified patients.
Areas with greater deprivation had the highest number of patients prescribed items for treating diabetes. There were 340,000 more patients receiving prescribing in the most deprived areas compared to the least deprived.
---
## 1.Things you should know
### 1.1. Background {.toc-ignore}
Diabetes is a lifelong condition that causes a person's blood sugar level to become too high. There are two main types of diabetes, type 1 diabetes where the body's immune system attacks and destroys the cells that produce insulin, and type 2 diabetes where the body does not produce enough insulin, or the body's cells do not react to insulin. Another form of diabetes is gestational diabetes, where diabetes develops during pregnancy and usually disappears after childbirth.
Chapter Three of the [NHS Long Term Plan](https://www.longtermplan.nhs.uk/) sets out the NHS's priorities for care quality and outcomes improvement for the decade ahead. There are also [5 long term highlights for diabetes](https://www.longtermplan.nhs.uk/blog/the-top-5-diabetes-long-term-highlights/) in the long term plan, aimed at prevention and treatment of this condition. These are:
* type 2 diabetes prevention as a priority
* expanding access to diabetes professionals for optimum treatment and care
* emphasising self-management of diabetes as a key role in 'upstream prevention'
* exploring low calorie diets as a potential treatment option for type 2 diabetes
* continuing digitisation of diabetes prevention, treatment, and care services.
This publication aims to describe the prescribing of medicines and appliances used for the treatment of diabetes in England that are dispensed in the community. This does not include data on medicines prescribed and dispensed in secondary care, prisons, or issued by a private prescriber as this is not held by the NHSBSA.
### 1.2. What is in these statistics {.toc-ignore}
These statistics detail:
* the total number of prescription items issued for drugs used in diabetes
* the number of identified patients that have received prescribing for these drugs
* demographic breakdowns of prescribing by age group and gender
* demographic breakdowns by gender
* demographic breakdowns by a measure of deprivation.
### 1.3. Classifications {.toc-ignore}
These statistics use the BNF therapeutic classifications defined in the British National Formulary (BNF) using the classification system prior to BNF edition 70. Each January the NHSBSA updates the classification of drugs within the BNF hierarchy which may involve some drugs changing classification between years of this publication. Five paragraphs of the BNF are covered within these statistics:
* Insulin (060101)
* Antidiabetic drugs (060102)
* Treatment of hypoglycaemia (060104)
* Diabetic diagnostic and monitoring agents (060106)
* Detection sensor interstitial fluid/gluc (2148)
These medicines are classified by their primary therapeutic indication. However, it is possible that they can be prescribed for other reasons outside of this primary therapeutic indication. For example, metformin which is used to reduce blood sugar for those with type 2 diabetes is also used to treat infertility caused by polycystic ovarian syndrome (PCOS).
It is not possible to distinguish the type of diabetes that insulin is prescribed for between patients. It is also not suitable to use drug types as an indicator of diabetes type.
Due to these reasons, these statistics may not give an accurate estimation of the population who are receiving drugs specifically for diabetes and the diabetes type of the population, any inferences made from this data should take this into consideration.
Hypodermic needles are not included as part of this publication as these are often used for other clinical conditions and would unnecessarily skew the data.
Only data for prescribed sensors for continuous blood glucose monitoring devices is included in these statistics, as readers are usually provided to patients separately.
### 1.4. Definitions {.toc-ignore}
::::{.row style="display: flex; margin-bottom: 24px;"}
:::{.col-md-12 .toc-ignore}
`r infoBox_border(header = "Item", text = "A single unit of medication listed separately on a prescription form. For example, in this publication, an item might be listed as Metformin 500mg tablets x28, distinct from other medications that may be prescribed on the same form.", width = "100%")`
:::
::::
::::{.row style="display: flex; margin-bottom: 24px;"}
:::{.col-md-12}
`r infoBox_border("NIC", text = "The Net Ingredient Cost (NIC) is the basic price of a single unit of a medication multiplied by the quantity prescribed. It does not include other fees incurred by dispensing contractors, such as controlled drug fees or the single activity fee. The basic price is determined by the [Drug Tariff](https://www.nhsbsa.nhs.uk/pharmacies-gp-practices-and-appliance-contractors/drug-tariff) or by the manufacturer, wholesaler, or supplier of the product.", width = "100%")`
:::
::::
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-12}
`r infoBox_border("Patient", text = "A unique NHS number captured from a prescription form or electronic prescription service (EPS) message.", width = "100%")`
:::
::::
### 1.5. Patient identification {.toc-ignore}
When the NHSBSA processes prescriptions it is not always possible to capture the NHS number of the patient. Table 1 shows the percentage of items for which a patient could be identified. This means that the data relating to patient counts represents most, but not all, patients.
Due to an increase in digital prescription processing through the Electronic Prescription Service (EPS) during the COVID-19 pandemic, more patients were identified in 2020/21, 2021/22 and 2022/23 compared to previous years. As patient identification rates increased, any increases in the number of identified patients between periods are likely to be an overestimate of the actual increase in patient numbers. This is because the percentage of patients who could be identified has increased. Conversely, any decrease over the same period is likely to be an underestimate of the actual decrease.
Where patients are identified, to assign them to a single age band their age is calculated on the 30 September of the given financial year. For patients where date of birth has not been captured, they have been included in an unknown category.
Gender information was not available from PDS for a small number of patients in each year, typically fewer than 100. This may be because it was not disclosed by the patient or not recorded by the organisation that collected the data.
These statistics do not include any information that is personally identifiable. You can find more information about how the NHSBSA protect personal information in the [confidentiality and access statement](https://www.nhsbsa.nhs.uk/policies-and-procedures).
**Table 1: The percentage of items for which an NHS number was recorded for listed BNF sections 2019/20 to 2023/24**
```{r capture_rates}
knitr::kable(table_1, align = "llrrrrr")
get_download_button(title = "Download table data", data = table_1_data, filename = "table_1")
```
Source: [Prescribing for diabetes summary tables - Costs and items (Patient identification rates)](`r config$costs_and_items_excel`)
---
## 2. Results and commentary
### 2.1. Volume and cost
#### Number of diabetes items prescribed and identified patients by financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 1: The number of items prescribed and identified patients have increased every year between 2015/16 and 2023/24
```{r}
figure_1
get_download_button(title = "Download table data", data = figure_1_data, filename = "figure_1")
```
##### Table
###### Table 2: The number of items prescribed and identified patients have increased every year between 2015/16 and 2023/24
```{r}
knitr::kable(table_2, align = "llr")
get_download_button(title = "Download table data", data = figure_1_data, filename = "table_2")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 1)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>71 million items for drugs used in diabetes prescribed in 2023/24.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>7% increase in items from 2022/23 to 2023/24.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>3.6 million identified patients were prescribed a diabetes item in 2023/24.</b>", width = "100%")`
:::
::::
There were 71 million items for drugs used in diabetes prescribed in 2023/24. This was a 42% increase from 50 million in 2015/16, and a 7% increase from 66 million items in 2022/23.
In 2023/24, an estimated 3.6 million identified patients received prescribing for drugs used in diabetes. This was a 34% increase from 2.7 million identified patients in 2015/16, and a 7% increase from 3.4 million in 2022/23.
#### Cost of diabetes prescribing by financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 2: The cost of diabetes prescribing has increased every year between 2015/16 and 2023/24
Note: The y-axis does not start at zero. This is to emphasise relative changes rather than absolute values. Please consider the scale when interpreting the data.
```{r}
figure_2
get_download_button(title = "Download chart data", data = figure_2_data, filename = "figure_2")
```
##### Table
###### Table 3: The cost of diabetes prescribing has increased every year between 2015/16 and 2023/24
```{r}
knitr::kable(table_3, align = "lr")
get_download_button(title = "Download table data", data = figure_2_data, filename = "table_3")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 1)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 the costs of prescribed drugs used in treating diabetes was £1.67 billion.</b>", width = "100%")`
:::
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>There was a 9% increase in costs from 2022/23 to 2023/24.</b>", width = "100%")`
:::
::::
Costs for drugs used in diabetes increased to £1.67 billion in 2023/24. This was a 74% increase from £960 million in 2015/16, and a 9% increase from £1.53 billion in 2022/23.
#### Percentage of all prescription items and costs for drugs used in diabetes by financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 3: The percentage of all prescription items that drugs used in diabetes have accounted for has increased each between 2015/16 and 2023/24
```{r}
figure_3
get_download_button(title = "Download chart data", data = figure_3_data, filename = "figure_3")
```
##### Table
###### Table 4: The percentage of all prescription items that drugs used in diabetes have accounted for has increased each between 2015/16 and 2023/24
```{r}
knitr::kable(table_4, align = "llr")
get_download_button(title = "Download table data", data = figure_3_data, filename = "table_4")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 1)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 the costs of prescribed drugs used in treating diabetes accounted for 15% of the total costs of all drugs prescribed.</b>", width = "100%")`
:::
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 prescribed drugs used in treating diabetes accounted for 6% of all items prescribed.</b>", width = "100%")`
:::
::::
The percentage of all prescription items that drugs used in diabetes have accounted for has increased each year, from 5% in 2015/16 to 6% in 2023/24.
In 2023/24, the cost of drugs used in diabetes accounted for 15% of the total spend on all items prescribed in England. This is an increase from 2015/16 when drugs used in diabetes accounted for 10% of the total spend.
#### Number of diabetes items prescribed by BNF paragraph by financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 4: Antidiabetic drugs are the most prescribed treatment for diabetes
```{r}
figure_4
get_download_button(title = "Download chart data", data = figure_4_data, filename = "figure_4")
```
##### Table
###### Table 5: Antidiabetic drugs are the most prescribed treatment for diabetes
```{r}
knitr::kable(table_5, align = "llr")
get_download_button(title = "Download table data", data = figure_4_data, filename = "table_5")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 2)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>53 million antidiabetic items prescribed in 2023/24.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>8% increase in antidiabetic items from 2022/23 to 2023/24.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>41% increase in glucose interstitial fluid detection sensor items from 2022/23 to 2023/24.</b>", width = "100%")`
:::
::::
Antidiabetic drugs, such as Metformin and Gliclazide, remain the most prescribed treatment for diabetes with 53 million items in 2023/24. This was a 49% increase from 35 million items in 2015/16, and an 8% increase from 49 million items in 2022/23.
From 2022/23 to 2023/24:
* prescribing of diabetic diagnostic and monitoring agents decreased by 5%
* insulin items increased by 4%
* medicines used to treat hypoglycemia increased by 4%
* glucose interstitial fluid detection sensor items increased by 41%
For additional details regarding the groups, drugs, and devices mentioned, please refer to Section 3 of this publication.
#### Cost of diabetes items prescribed by BNF paragraph and financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 5: Antidiabetic drugs have the highest costs for treatments prescribed for diabetes
```{r}
figure_5
get_download_button(title = "Download chart data", data = figure_5_data, filename = "figure_5")
```
##### Table
###### Table 6: Antidiabetic drugs have the highest costs for treatments prescribed for diabetes
```{r}
knitr::kable(table_6, align = "llr")
get_download_button(title = "Download table data", data = figure_5_data, filename = "table_6")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 2)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>Costs of antidiabetic items prescribed in 2023/24 was £960 million.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>10% increase in antidiabetic drug costs from 2022/23 to 2023/24.</b>", width = "100%")`
:::
:::{.col-md-4}
`r infoBox_no_border(header = "", text = "<b>38% increase in costs of glucose interstitial fluid detection sensors prescribed from 2022/23 to 2023/24.</b>", width = "100%")`
:::
::::
In 2023/24, antidiabetic drugs had a cost of £960 million. This was an increase of 127% from £420 million in 2015/16, and an 10% increase from £880 million in 2022/23. These increases in cost are much greater than the respective increases in the number of prescribed items.
From 2022/23 to 2023/24:
* costs of prescribing diabetic diagnostic and monitoring agents decreased by 13%
* costs of insulin items increased by 2%
* costs of medicines used to treat hypoglycemia increased by 11%
* costs of glucose interstitial fluid detection sensor items increased by 38%
#### Average number of diabetes items per patient by financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 6: The average number of diabetes items per patient increased year on year between 2015/16 and 2019/20 but has remained consistent between 2019/20 and 2023/24
Note: The y-axis does not start at zero. This is to emphasise relative changes rather than absolute values. Please consider the scale when interpreting the data.
```{r}
figure_6
get_download_button(title = "Download chart data", data = figure_6_data, filename = "figure_6")
```
##### Table
###### Table 7: The average number of diabetes items per patient increased year on year between 2015/16 and 2019/20 but has remained consistent between 2019/20 and 2023/24
```{r}
knitr::kable(table_7, align = "lrrr")
get_download_button(title = "Download table data", data = figure_6_data, filename = "table_7")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 1)](`r config$costs_and_items_excel`)
The average number of items per patient increased each year between 2015/16 and 2019/20, from 17.5 diabetes items per patient to 19.1. Between 2019/20 and 2023/24 the rate has remained consistent with only a 0.2 variation in the average number of items per patient.
This measure only includes prescribing of drugs used in diabetes and does not include any items prescribed from other BNF sections.
#### Cost of diabetes prescribing per patient by Integrated Care Board (ICB) {.tabset}
<div class = "tabset">
##### Chart
###### Figure 7: The median costs per patient by ICB in 2023/24 was £460
```{r}
figure_7
get_download_button(title = "Download chart data", data = figure_7_data_raw, filename = "figure_7")
```
##### Table
###### Table 8: The median costs per patient by ICB in 2023/24 was £460
```{r}
knitr::kable(table_8, align = "lrrrr")
get_download_button(title = "Download table data", data = figure_7_data_raw, filename = "table_8")
```
</div>
Source: [Prescribing for diabetes summary tables - Costs and items (Table 4)](`r config$costs_and_items_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>Maximum costs per patient by ICB in 2023/24 was £508.</b>", width = "100%")`
:::
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>Minimum costs per patient by ICB in 2023/24 was £387.</b>", width = "100%")`
:::
::::
See section 4.7. Interpretation of a box plot for guidance on interpreting this chart.
The cost per patient varied across ICBs in 2023/24, from £387 to £508 with the median cost per patient per ICB being £460. There were no ICBs that were an outlier for cost per patient for 2023/24.
### 2.2. Patient demographics
#### Number of identified patients receiving diabetes prescribing by gender and financial year {.tabset}
<div class = "tabset">
##### Chart
###### Figure 8: The overall split of male and female patients has remained consistent between 2015/16 and 2023/24
```{r}
figure_8
get_download_button(title = "Download chart data", data = figure_8_data, filename = "figure_8")
```
##### Table
###### Table 9: The overall split of male and female patients has remained consistent between 2015/16 and 2023/24
```{r}
knitr::kable(table_9, align = "llr")
get_download_button(title = "Download table data", data = figure_8_data, filename = "table_9")
```
</div>
Source: [Prescribing for diabetes summary tables - Patient demographics (Table 2)](`r config$demographics_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 55% of identified patients were male.</b>", width = "100%")`
:::
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 45% of identified patients were female.</b>", width = "100%")`
:::
::::
While the overall number of identified patients receiving drugs used in diabetes prescribing has increased year-on-year, the overall split of male and female patients has remained consistent.
In 2015/16, 45% of identified patients where gender was known were female and 55% were male. This is the same percentage to 2023/24. However, there are an additional 410,000 female identified patients than there were in 2015/16, and an additional 520,000 males.
Identified patients where their gender was unknown or indeterminate have been grouped together and can be found in the summary tables that accompany this release.
#### Number of identified patients receiving diabetes prescribing by age and gender {.tabset}
<div class = "tabset">
##### Chart
###### Figure 9: Male patients aged 60 to 64 was the largest prescribing group for drugs used in diabetes in 2023/24
```{r}
figure_9
get_download_button(title = "Download chart data", data = figure_9_data, filename = "figure_9")
```
##### Table
###### Table 10: Male patients aged 60 to 64 was the largest prescribing group for drugs used in diabetes in 2023/24
```{r}
knitr::kable(table_10, align = "llr")
get_download_button(title = "Download table data", data = figure_9_data, filename = "table_10")
```
</div>
Source: [Prescribing for diabetes summary tables - Patient demographics (Table 6)](`r config$demographics_excel`)
In 2023/24, 270,000 identified patients were male aged 60 to 64, 7% of all identified patients where a gender was known. This was the most common group to receive prescribing for drugs used in diabetes, while male patients aged 65 to 69 were the second most common group with 260,000 identified patients.
More information on how we calculate a patient's age can be found in section 4 of this summary.
#### **Table 11: Number of child and adult identified patients receiving diabetes prescribing (millions of patients)**
```{r patients_by_adult_child}
knitr::kable(table_11, align = "lrrrrr")
get_download_button(title = "Download table data", data = table_11_data, filename = "table_11")
```
Source: [Prescribing for diabetes summary tables - Patient demographics (Table 8)](`r config$demographics_excel`)
There were 38,000 identified patients aged 17 and under that received prescribing for drugs used in diabetes in 2023/24. This was 1% of all identified patients with a captured age.
#### Number of identified patients receiving diabetes prescribing by IMD quintile {.tabset}
<div class = "tabset">
##### Chart
###### Figure 10: More people were prescribed drugs used in diabetes in more deprived areas
```{r}
figure_10
get_download_button(title = "Download chart data", data = figure_10_data, filename = "figure_10")
```
##### Table
###### Table 12: More people were prescribed drugs used in diabetes in more deprived areas
```{r}
knitr::kable(table_12, align = "lr")
get_download_button(title = "Download table data", data = figure_10_data, filename = "table_12")
```
</div>
Source: [Prescribing for diabetes summary tables - Patient demographics (Table 9)](`r config$demographics_excel`)
::::{.row style="display: flex; padding-bottom: 10px;"}
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 920,000 patients were from the most deprived areas in England.</b>", width = "100%")`
:::
:::{.col-md-6}
`r infoBox_no_border(header = "", text = "<b>In 2023/24 590,000 patients were from the least deprived areas in England.</b>", width = "100%")`
:::
::::
In 2023/24, there were 920,000 identified patients prescribed drugs in the most deprived areas in England, 340,000 more than the 590,000 identified patients in the least deprived areas. In general, more people were prescribed drugs used in diabetes in more deprived areas in 2023/24. This pattern has remained consistent since 2015/16.
The English Indices of Deprivation have been used in this publication to provide a measure of patient deprivation. The patient’s postcode has been used to assigned them to an IMD quintile. You can find more information about this in section 4 of this summary.
---
## 3. Background
### 3.1. Antidiabetic drugs {.toc-ignore}
Antidiabetic drugs are generally used to treat type 2 diabetes. They are taken by mouth and work in a number of different ways depending on the type of drug, for example by increasing the amount of insulin made in the body or by decreasing the production of glucose in the body and so lowering blood glucose levels. Patients may be prescribed a single antidiabetic drug or may be prescribed several to work together to achieve the desired control of their diabetes in combination with diet, exercise and lifestyle advice and interventions.
One of the most commonly used antidiabetic drugs is metformin which is usually the first antidiabetic drug a patient with type 2 diabetes will be prescribed. Depending on the response to treatment, additional antidiabetic drugs may be added, and the patient may be prescribed insulin further into treatment if the desired control of the diabetes is not achieved with antidiabetic drugs and diet and lifestyle management alone.
You can find out more information on [type 2 diabetes medications](https://www.nhs.uk/conditions/type-2-diabetes/understanding-medication/) on the NHS website.
### 3.2. Insulin {.toc-ignore}
Insulin is a hormone that plays a key role in the body’s metabolism, including regulating blood glucose levels. In those with type 1 diabetes, the body produces insufficient insulin to undertake this role effectively. For those with type 2 diabetes, the body does not respond effectively to insulin (known as insulin resistance) or the body does not make enough insulin. People with type 1 diabetes will be prescribed insulin, while only a proportion of those with type 2 diabetes will be prescribed this.
Insulin can be injected or delivered by an insulin pump which regularly infuses insulin into the body. Synthetic insulin or non-synthetic animal insulin are available depending on the needs of the patient. Insulin preparations also vary in how quickly they act and are often used in combinations, depending on the individual requirements of a patient.
You can find out more about [insulin](https://www.nhs.uk/conditions/type-1-diabetes/about-insulin/) on the NHS website.
### 3.3. Diabetes diagnostic and monitoring agents {.toc-ignore}
Diabetes diagnostic and monitoring agents cover a range of monitoring equipment and testing strips that can be used by a person with diabetes to check their diabetic control. Self-monitoring is not routinely suggested for type 2 diabetes, but it is an integral part of treatment for people with type 1 diabetes. This can be monitoring blood glucose levels or monitoring ketones (a by-product of the breakdown of fats) in either the blood or urine.
By being able to monitor blood glucose levels, patients can manage their health effectively and prevent hypoglycaemia or hyperglycaemia.
Diabetic patients can be at risk of diabetic ketoacidosis, a serious condition, where the body starts to run out of insulin and ketones build up in the body. Checking ketone levels can be an important early warning of this and monitoring agents allow people to check these levels themselves.
### 3.4. Treatment of hypoglycaemia {.toc-ignore}
Hypoglycaemia is a lower than normal blood glucose concentration. It is the most common side effect of insulin treatment and can cause acute symptoms such as feeling tired and sweating, drowsiness and confusion. This can progress to seizures or unconsciousness if untreated.
Hypoglycaemia can be treated with a sugary drink or snack, though this is not always enough. Fast-acting carbohydrates are prescribed for patients to keep at hand in case of hypoglycaemia, these can be oral liquids and gels, capsules or even injectables.
The NHS website has additional information on [symptoms and treatment of hypoglycaemia](https://www.nhs.uk/conditions/low-blood-sugar-hypoglycaemia/).
### 3.5 Glucose interstitial fluid detection sensors {.toc-ignore}
Glucose interstitial fluid detection sensors are a specific type of diabetic monitoring equipment that allow for real time testing of glucose levels without finger pricks or test strips.
NICE guidance was updated in [March 2022](https://www.nice.org.uk/guidance/ng17) to recommended the use of real-time continuous glucose monitoring (CGM) for all adults and children living with type 1 diabetes.
Instead of using blood the testing monitors the amount of glucose in the fluid surrounding a patients cells. This is called interstitial fluid. This measurement is done using a sensor on the body that can be read using a specific reader or with a smart phone.
The readings from interstitial fluid can lag behind blood sugar by up to 15 minutes so the use of sensors does not entirely replace other diabetic monitoring such as testing strips though it would usually reduce the requirements for such.
Only data for prescribed sensors is included in this report as readers are usually provided separately.
The NHS website has additional information on [continuous glucose monitoring.](https://www.nhs.uk/conditions/type-1-diabetes/managing-blood-glucose-levels/continuous-glucose-monitoring-cgm-and-flash/)
---
## 4. About these statistics
Further information on the methodology used in this publication and further background information is available in our [Background Information and Methodology](`r config$background_link`) supporting document.
### 4.1. Patient counts {.toc-ignore}
The patient counts shown in these statistics should only be analysed at the level at which they are presented. Adding together any patient counts is likely to result in an overestimate of the number of patients. A person will be included, or counted, in each category or time period in which they received relevant prescriptions. For example, if a patient received a prescription item for a diabetes product in 2018/19 and another in 2019/20, then adding together those totals would count that patient twice. For the same reason, data on patient counts for different BNF paragraphs should not be added together.
### 4.2. Patient age and gender {.toc-ignore}
The age and gender of patients used in these statistics is derived from data provided by the NHS Personal Demographics Service (PDS) for NHS numbers that have been successfully verified by them. A patient's age, used to assign them to an age group, has been calculated on 30 September for the given financial year. It is possible that a patient's PDS information may change over the course of the year, in these cases patients may be subject to multiple counting in these analyses.
### 4.3. Index of deprivation {.toc-ignore}
The English Indices of Deprivation 2019 have been used to provide a measure of patient deprivation. The English Indices of Deprivation are an official national measure of deprivation that follows an established methodological framework to capture a wide range of individuals living conditions.
IMD deciles are calculated by ranking census lower-layer super output areas (LSOAs) from most deprived to least deprived and dividing them into 10 equal groups. These range from the most deprived 10% (decile 1) of small areas nationally to the least deprived 10% (decile 10) of small areas nationally. We have aggregated these deciles into quintiles in this publication, for use alongside the [NHS Core20PLUS5 approach](https://www.england.nhs.uk/about/equality/equality-hub/core20plus5/).
The reported IMD quintile is derived from the postcode of the patient an item has been prescribed to. When a patient postcode is unknown but we hold a postcode for the prescribing practice, this will be used instead. Quintile 1 represents the 20% most deprived areas and quintile 5 is the 20% least deprived areas. There are a small number of items each year that we have reported as having an unknown IMD quintile. These are items where we have been unable to match the patient postcode or practice postcode to a postcode in the NSPL May 2024 edition.
### 4.4. Geographies included in this publication {.toc-ignore}
The patient deprivation measures given in these statistics are based upon the LSOA of the postcode of the patient as matched to the May 2023 NSPL file. However, higher geographies included in the statistical summary tables of this publication, such as ICB, use NHSBSA administrative records, not geographical boundaries, and more closely reflect the operational organisation of practices than other geographical data sources.
### 4.5. Changes made to this publication {.toc-ignore}
For this release we have changed some of the underlying methodology used to join patient demographic data to our prescriptions data. This has been done to align with best practices in reporting patient gender, and to be consistent with other statistical outputs from across the NHS. When we can match an identified patient’s NHS number with Patient Demographic Service (PDS) data, we now use the latest recorded gender for that patient. If we receive new information on gender for a patient, this will be applied to all previous prescribing for the patient. For example, this will affect data involving patients whose gender was previously not known in PDS, but subsequently becomes known. This may impact historical figures as patients can move between gender categories.
The ICB level tables included in the supporting tables for this release now include statistics on the costs and items for non-diabetes prescribing.
For full details of the changes made please refer to the Background Information and Methodology note released alongside these statistics.
### 4.6. Interpretation of a box plot {.toc-ignore}
Box plots are used to help visualise not just averages, but also how data is spread out. If all data points were arranged from the smallest to the biggest, halfway along this line in the middle would be the median. The middle line in the box is the median. The top section of the box includes the 25% of numbers directly above the median, the bottom includes the 25% directly below the median.
The remaining lowest 25% and highest 25% are usually captured by the whiskers; the whiskers are set to have a maximum length of 1.5 interquartile ranges (the length of the box), the end of each whisker is the most extreme value within this range. Any points that are further than 1.5 interquartile ranges from the top or bottom of the box are classified as statistical outliers and are shown as small circles on the box plot.
The length of the whiskers away from the median show how similar the data is compared to the average; short whiskers indicate that there are no areas extremely different to the average whereas long whiskers show the data is much more spread out and there are bigger differences between the highest and lowest numbers.
---
## 5. Rounding
The high-level figures in this statistical summary have been rounded as per the table below:
```{r rounding_table}
rounding_table <- data.table(
"From" = c("0", "1,001", "10,001", "100,001", "1,000,001", "10,000,001", "100,000,001", "10,000,000,001"),
"To" = c("1,000", "10,000", "100,000", "1,000,000", "10,000,000", "100,000,000", "10,000,000,000", "100,000,000,000"),
"Round to nearest" = c("1", "100", "1,000", "10,000", "100,000", "1,000,000", "10,000,000", "100,000,000")
)
knitr::kable(rounding_table, align = "rrr")
```
All changes and totals are calculated prior to rounding. Percentage changes are calculated prior to rounding and then are rounded to the nearest whole number. As all figures within this statistical summary have been rounded, they may not match totals elsewhere when aggregated.
The summary tables released with this publication allow users to investigate this data at lower levels of granularity. Figures in the supplementary tables have not been rounded.
---
## 6. Statistical disclosure control
Statistical disclosure control has been applied to these statistics. Patient count, items, and net ingredient cost (NIC) have been redacted in the supporting summary tables if they relate to fewer than 5 patients. Further information about our statistical disclosure control protocol [can be found on our website](https://www.nhsbsa.nhs.uk/policies-and-procedures).
The high-level figures in this statistical summary have been rounded where appropriate for clarity. This is to make this narrative as accessible as possible to all readers. The summary tables released with this publication allow users to investigate this data at lower levels of granularity. Figures in the supplementary tables have not been rounded.
---
## 7. Accessibility
If you need information on this website in a different format, such as accessible PDF, large print, easy read, audio recording or braille, you can contact us by:
**Email**: [email protected]
**Telephone**: 0191 203 5318
[Find out about call charges](https://www.nhsbsa.nhs.uk/contact-us/call-charges-and-phone-numbers)
We will consider your request and get back to you in 5 working days.
These contact details are only for accessibility queries. This email address is not for technical queries or IT problems. If you have a query that is not about accessibility, go to the ‘Feedback and contact us’ section of this page.
Read our [Accessibility statement for Official Statistics Narratives](https://www.nhsbsa.nhs.uk/accessibility-statement-official-statistics-narratives).
---
## 8. Feedback and contact us
Feedback is important to us. We welcome any questions and comments relating to these statistics.
You can complete a [short survey about this publication](`r config$stats_survey_link`) to help us improve the Official Statistics that we produce. All responses will remain anonymous, and individuals will not be identifiable in any report that we produce.
You can view our [privacy policy](https://www.nhsbsa.nhs.uk/our-policies/privacy) on our website to see how your data is used and stored.
You can contact us by:
**Email:** [email protected]
**You can also write to us at:**
NHSBSA - Statistics
NHS Business Services Authority
Stella House
Goldcrest Way
Newburn Riverside
Newcastle upon Tyne
NE15 8NY
**Responsible statistician:** `r config$responsible_statistician`
</main>