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hrt-narrative.Rmd
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---
title: "Hormone replacement therapy (HRT)"
pagetitle: HRT England monthly summary
always_allow_html: true
output:
word_document:
toc: yes
toc_depth: '3'
html_document:
anchor_sections: no
css: www/style.css
toc: yes
toc_depth: 3
toc_float:
collapsed: no
---
<script>
$(document).ready(function() {
$('#header').prepend('<img src=`r knitr::image_uri("www/bsa_logo.svg")` alt=\"NHSBSA logo\" style=\"position:relative; float: right; top: 0; right: 0; padding: 10px; width: 25%;\">');
});
</script>
<style type="text/css">
body, td {
font-size: 16px;
font-family: sans-serif;
}
</style>
<html lang="en">
```{r setup, include=FALSE}
# set code chunk options to disable echo by default
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
message = FALSE)
```
<main>
# England April 2015 to `r ltst_month_tidy` {.toc-ignore}
Published 6 October 2022
## Key findings
* In 2021/22, there were 7.80 million items for HRT prescribed in England, a 35.0% increase from 2020/21.
* There were an estimated 1.93 million identified patients that were prescribed HRT in England in 2021/22. This was a 30.5% increase from an estimated 1.48 million identified patients in 2020/21.
* Areas of greater deprivation had the lowest number of estimated identified patients who were being prescribed HRT in 2021/22, almost twice as many patients receiving prescribing in the least deprived areas of the country compared to the most deprived.
* A greater proportion of the estimated identified patients from the most deprived areas of the country who received HRT prescribing in 2021/22 were under the age of 60. A total of 76.6% of the estimated identified patients in the most deprived areas were under 60, 64.6% of the estimated identified patients from the least deprived areas were under 60.
---
## 1. Introduction
### 1.1. Scope {.toc-ignore}
Hormone replacement therapy (HRT) is a treatment to relieve symptoms of the menopause. It replaces hormones that are at a lower level during the perimenopause, menopause, and postmenopause.
Millions of women go through the menopause every year, many women experience some symptoms, such as vaginal dryness, low mood, anxiety, hot flushes and difficulty sleeping. Some of these symptoms can be severe and have a negative impact on everyday life.
This Official Statistics publication has been released to support the work being undertaken by the Department of Health and Social Care, NHS England, and HRT suppliers.
This publication aims to describe the prescribing of HRT medications in England that are subsequently dispensed in the community in England, Scotland, Wales, Isle of Man or the Channel Islands by a pharmacy, appliance contractor, dispensing doctor, or have been personally administered by a GP practice. They do not include data on medicines used in secondary care, prisons, or issued by a private prescriber. The statistics do not include prescriptions that were issued but not presented for dispensing or where there has been an unmet demand for treatment.
These statistics detail:
* the total number of prescription items issued for drugs used in HRT
* the number of identified patients that have received prescribing for these drugs
* demographic breakdowns of prescribing by age group
* demographic breakdowns by a measure of deprivation.
The costs associated with the provision of these medicines are not discussed in this summary. However, they are available within the supporting summary tables that accompany this release.
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 data. Two paragraphs of the British National Formulary (BNF) are covered within these statistics:
* 6.4.1 - female sex hormones and their modulators
* 7.2.1 - preparations for vaginal and vulval changes
There is not one singular definition of HRT medications and many other products may be used in the treatment of the menopause that are not included in these statistics.
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, the treatment of gender dysphoria. The clinical indication of a prescription is not captured by NHSBSA during processing, and therefore we cannot determine the reason that a prescription was issued.
### 1.2. Definitions {.toc-ignore}
Throughout this publication the term ‘item’, short for ‘prescription item’, means a single instance of a drug that is listed as a separate entry on a prescription form. For example, Norethisterone 5mg tablets x28. The term 'quantity' refers to the physical quantity of any items which have been prescribed. For example, Norethisterone 5mg tablets x28 would be counted as one item but the quantity would be 28.
In this release the term 'patient/s' is used to mean a unique, valid NHS number that has been captured from a prescription form and subsequently verified by the NHS Personal Demographics Service (PDS). Across 2020/21, use of the Electronic Prescription Service (EPS) increased considerably during the COVID-19 pandemic. As this process captures NHS numbers digitally rather than through scanned paper forms, this has naturally led to an increase in the proportions of identified patients for both 2020/21 and 2021/22 than seen in previous years.
Where patients are identified, their age is calculated on 30 September of the given financial year to assign them to a single age band. For patients where date of birth has not been captured, they have been included in a category of unknown.
There are many costs incurred when a dispensing contractor fulfils a prescription, such as the single activity fee or controlled drug fees. The costs reported in this publication represent the basic price of the item and the quantity prescribed. This is sometimes called the 'Net Ingredient Cost' (NIC). The basic price is given either in the Drug Tariff or is determined from prices published by manufacturers, wholesalers or suppliers. Basic price is set out in Parts VIII and IX of the [Drug Tariff](https://www.nhsbsa.nhs.uk/pharmacies-gp-practices-and-appliance-contractors/drug-tariff). For any drugs not in Part VIII, the price is usually taken from the manufacturer, wholesaler or supplier of the product.
### 1.3. Time periods {.toc-ignore}
This publication presents data from April 2015 to `r ltst_month_tidy`, the most recent available. The data is available in tables at both a monthly and a financial year level. The most recent financial year, 2022/23, shows a year to date view of the figures.
### 1.4. Patient identification {.toc-ignore}
When the NHSBSA processes prescriptions, the NHS number of the patient is identified for most, but not all, prescriptions. The proportion of items for which a patient could be identified is shown in Table 1. This means that data relating to patient counts represents most, but not all, patients.
These statistics do not contain any personally identifiable data. More information about how the NHSBSA protects personal information can be found in the [statement on confidentiality and access](https://www.nhsbsa.nhs.uk/policies-and-procedures).
#### Table 1: The proportion of items for which an NHS number was recorded for listed BNF paragraphs
```{r pat_ident_rates, out.width="100%"}
max_col <- ncol(raw_data$pi_table_annual)
#get this number plus previous 4
seq <- seq(max_col - 4, max_col)
#get number of columns
cols <- length(seq) + 2
names(raw_data$pi_table_annual)[max_col] <- paste0(
substr(names(raw_data$pi_table_annual)[max_col],1,9), " (YTD ", ltst_month_tidy,")"
)
raw_data$pi_table_annual %>%
select(1, 2, unlist(as.numeric(seq))) %>%
mutate_if(is.numeric, signif, 3) %>%
mutate_if(is.numeric, paste0, "%") %>%
DT::datatable(rownames = FALSE,
options = list(dom = "t",
columnDefs = list(
list(orderable = FALSE,
targets = "_all"),
list(className = "dt-left", targets = 0:1),
list(className = "dt-right",
targets = 3:cols - 1)
)))
```
---
## 2. Results and commentary
### Figure 1: Number of prescribed items and estimated identified patients by financial year {.toc-ignore}
```{r annual_summary, out.width="100%"}
raw_data$national_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
dplyr::group_by(FINANCIAL_YEAR) %>%
dplyr::summarise(`Prescribed items` = sum(ITEM_COUNT),
`Identified patients` = sum(PATIENT_COUNT),
.groups = "drop") %>%
tidyr::pivot_longer(cols = c(`Identified patients`,`Prescribed items`),
names_to = "measure",
values_to = "value") %>%
dplyr::mutate(value = signif(value, 3)) %>%
dplyr::arrange(desc(measure)) %>%
group_chart_hc(
x = FINANCIAL_YEAR,
y = value,
group = measure,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Volume",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE)%>%
hc_xAxis(
plotLines = list(
list(
value = 6,
color = "grey",
width = 1,
dashStyle = "dash",
label = list(
rotation = 0,
text = "<b>Note:</b> The figures <br>beyond this point <br>represent<br>prescribing in <br>April 2022 to <br>June 2022 only",
style = list(
fontSize = "10px"
)
)
))
)
```
Source: [Financial Year Summary Tables - National Totals table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
There were 7.80 million HRT drug items prescribed in 2021/22. This was a 35.0% increase from 5.78 million items compared with the previous year.
There were an estimated 1.93 million identified patients who were prescribed at least one HRT item in 2021/22. This was a 30.5% increase from 1.48 million identified patients when compared with 2020/21.
From April 2022 to June 2022 there were 2.57 million HRT drug items prescribed. This is 920,000 more items than the same period in the previous year, a 55.8% increase.
### Figure 2: Number of prescribed items and estimated identified patients by month {.toc-ignore}
```{r monthly_summary, out.width="100%"}
raw_data$national_monthly %>%
filter(YEAR_MONTH >= lt_st_month_min) %>%
dplyr::group_by(YEAR_MONTH) %>%
dplyr::summarise(`Prescribed items` = sum(ITEM_COUNT),
`Identified patients` = sum(PATIENT_COUNT),
.groups = "drop") %>%
tidyr::pivot_longer(cols = c(`Identified patients`,`Prescribed items`),
names_to = "measure",
values_to = "value") %>%
dplyr::mutate(value = signif(value, 3)) %>%
dplyr::arrange(desc(measure)) %>%
mutate(
YEAR_MONTH = base::as.Date(as.character(paste0(YEAR_MONTH,"01")), format = "%Y%m%d")
) %>%
group_chart_hc(
x = YEAR_MONTH,
y = value,
group = measure,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Month",
yLab = "Volume",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE) %>%
hc_xAxis(type = "datetime")
```
Source: [Monthly Summary Tables - National Totals table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_monthly_June_2022_v001.xlsx)
There were 870,000 HRT drug items prescribed in June 2022. This was a 4.02% decrease from 906,000 items compared with the previous month, and an increase of 270,000 (45.1%) from 600,000 items in June 2021. Prescribing of HRT drug items has been increasing since July 2020, with 373,000 more items prescribed in June 2022 when compared to July 2020, an increase of 75.1% over the period.
There were an estimated 665,000 identified patients who were prescribed at least one HRT item in June 2022. This was a 4.34% decrease from 695,000 identified patients when compared with the previous month. There were 251,000 more identified patients who received an HRT item in June 2022 compared to July 2020, an increase of 60.7%.
### Figure 3: Number of prescribed items and estimated identified patients by financial year and BNF paragraph {.toc-ignore}
```{r, figures-side, fig.show="hold", out.width="50%"}
chart_1_data <- raw_data$national_par_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
filter(PARAGRAPH_CODE == "060401") %>%
group_by(FINANCIAL_YEAR) %>%
dplyr::summarise(`Prescribed items` = sum(ITEM_COUNT),
`Identified patients` = sum(PATIENT_COUNT),
.groups = "drop") %>%
tidyr::pivot_longer(cols = c(`Identified patients`,`Prescribed items`),
names_to = "measure",
values_to = "value") %>%
dplyr::mutate(value = signif(value, 3)) %>%
dplyr::arrange(desc(measure))
chart_1 <- chart_1_data %>%
group_chart_hc(
x = FINANCIAL_YEAR,
y = value,
group = measure,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Volume",
title = "BNF Paragraph - 060401"
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE) %>%
hc_title(align = "left") %>%
hc_yAxis(max = max(chart_1_data$value)) %>%
hc_xAxis(
plotLines = list(
list(
value = 6,
color = "grey",
width = 1,
dashStyle = "dash",
label = list(
rotation = 0,
text = "<b>Note:</b> The figures <br>beyond this point <br>represent<br>prescribing in <br>April 2022 to <br>June 2022 only",
style = list(
fontSize = "8px"
)
)
))
)
chart_2_data <- raw_data$national_par_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
filter(PARAGRAPH_CODE == "070201") %>%
group_by(FINANCIAL_YEAR) %>%
dplyr::summarise(`Prescribed items` = sum(ITEM_COUNT),
`Identified patients` = sum(PATIENT_COUNT),
.groups = "drop") %>%
tidyr::pivot_longer(cols = c(`Identified patients`,`Prescribed items`),
names_to = "measure",
values_to = "value") %>%
dplyr::mutate(value = signif(value, 3)) %>%
dplyr::arrange(desc(measure))
chart_2 <- chart_2_data %>%
group_chart_hc(
x = FINANCIAL_YEAR,
y = value,
group = measure,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Volume",
title = "BNF Paragraph - 070201"
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE)%>%
hc_title(align = "left") %>%
hc_yAxis(max = max(chart_1_data$value)) %>%
hc_xAxis(
plotLines = list(
list(
value = 6,
color = "grey",
width = 1,
dashStyle = "dash",
label = list(
rotation = 0,
text = "<b>Note:</b> The figures <br>beyond this point <br>represent<br>prescribing in <br>April 2022 to <br>June 2022 only",
style = list(
fontSize = "8px"
)
)
))
)
hw_grid(chart_1, chart_2)
```
Source: [Financial Year Summary Tables - BNF Paragraph table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
There were 5.75 million female sex hormones and their modulators items prescribed in 2021/22. This was a 41.1% increase from 4.07 million items compared with the previous year.
There were an estimated 1.36 million identified patients who were prescribed at least one female sex hormones and their modulators item in 2021/22. This was a 38.8% increase from 983,000 identified patients when compared with 2020/21.
From April 2022 to June 2022 there were 1.98 million female sex hormones and their modulators items prescribed. This is 804,000 more items than the period April 2021 to June 2021, a 68.3% increase.
There were 2.05 million preparations for vaginal and vulval changes items prescribed in 2021/22. This was a 20.4% increase from 1.71 million items compared with the previous year.
There were an estimated 682,000 identified patients who were prescribed at least one preparations for vaginal and vulval changes item in 2021/22. This was a 22.6% increase from 556,000 identified patients when compared with 2020/21.
From April 2022 to June 2022 were 587,000 million preparations for vaginal and vulval changes items prescribed. This is 116,000 more items than the period April 2021 to June 2021, a 24.6% increase.
### Figure 4: Estimated Number of identified patients by financial year for selected BNF chemical substances {.toc-ignore}
A chemical substance is the name of the main active ingredient in a drug.
```{r annual_chem_sub, out.width="100%"}
chart_data <- raw_data$chem_sub_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
filter(
CHEM_SUB_NAME %in% c("Estradiol", "Estriol", "Estradiol with progestogen", "Progesterone", "Norethisterone")
) %>%
mutate(CHEM_SUB = paste0(CHEM_SUB_NAME, " - ", CHEM_SUB_CODE)) %>%
select(1, 12, 9) %>%
filter(
PATIENT_COUNT > 0
) %>%
mutate(value = signif(PATIENT_COUNT, 3))
chart_data %>%
group_chart_hc(
x = FINANCIAL_YEAR,
y = value,
group = CHEM_SUB,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Volume",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE)%>%
hc_title(align = "left") %>%
hc_xAxis(
plotLines = list(
list(
value = 6,
color = "grey",
width = 1,
dashStyle = "dash",
label = list(
rotation = 0,
text = "<b>Note:</b> The figures <br>beyond this point <br>represent<br>prescribing in <br>April 2022 to <br>June 2022 only",
style = list(
fontSize = "10px"
)
)
))
)%>%
hc_colors(c("#ED8B00", "#41B6E6", "#006747", "#78BE20", "#003087", "#AE2573"))
```
Source: [Financial Year Summary Tables - Chemical Substance table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
Between 2020/21 and 2021/22 Estradiol (0604011G0) was the chemical substance which had the largest increase in estimated identified patients who were prescribed at least one item. This was an increase of 59.2% or 220,000 patients from 272,000 in 2020/21 to 592,000 in 2021/22.
### Figure 5: Number of prescribed items by financial year for selected BNF chemical substances {.toc-ignore}
```{r chem_sub_items, out.width="100%"}
chart_data <- raw_data$chem_sub_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
filter(
CHEM_SUB_NAME %in% c("Estradiol", "Estriol", "Estradiol with progestogen", "Progesterone", "Norethisterone")
) %>%
mutate(CHEM_SUB = paste0(CHEM_SUB_NAME, " - ", CHEM_SUB_CODE)) %>%
filter(CHEM_SUB != "Estriol - 0604011M0") %>%
select(1, 12, 10) %>%
group_by(FINANCIAL_YEAR, CHEM_SUB) %>%
summarise(total = sum(ITEM_COUNT, na.rm = T))%>%
mutate(total = signif(total, 3))
chart_data %>%
group_chart_hc(
x = FINANCIAL_YEAR,
y = total,
group = CHEM_SUB,
type = "line",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Volume",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE)%>%
hc_title(align = "left") %>%
hc_xAxis(
plotLines = list(
list(
value = 6,
color = "grey",
width = 1,
dashStyle = "dash",
label = list(
rotation = 0,
text = "<b>Note:</b> The figures <br>beyond this point <br>represent<br>prescribing in <br>April 2022 to <br>June 2022 only",
style = list(
fontSize = "10px"
)
)
))
)%>%
hc_colors(c("#ED8B00", "#41B6E6", "#006747", "#78BE20", "#003087", "#AE2573"))
```
Source: [Financial Year Summary Tables - Chemical Substance table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
Between 2020/21 and 2021/22 Estradiol (0604011G0) was the chemical substance which had the largest increase in the number of items prescribed. This was an increase of 48.4% or 760,000 items from 1.57 million in 2020/21 to 2.33 million in 2021/22.
### Table 2: Top 10 presentations by prescribed items in `r ltst_year`{.toc-ignore}
Pharmaceutical presentations can come in many forms including tablets, capsules, patches, gels, creams and more. Please see the definition of items and quantity given in section 1.2 to help interpret tables 2 and 3. The chemical substance listed in figures 4 and 5 can be made up of more than one of the presentations in tables 2 and 3. For example both Vagifem 10 microgram vaginal tablets and Estradiol 10 microgram pessaries have the chemical substance of Estradiol.
Tables 2 and 3 are based on the BNF presentation that has appeared on the prescription form. It is possible that products that are different brands, or generic equivalents, of the same product may appear in these tables. For example Oestrogel Pump-Pack 0.06% gel is the branded equivalent of Estradiol 0.06% gel (750 microgram per actuation) and Vagifem 10 microgram vaginal tablets is the branded equivalent of Estradiol 10 microgram pessaries.
``` {r top_pres_fy, out.width = "100%"}
presentation_annual %>%
filter(`Financial Year` == ltst_year) %>%
top_n(10, `Total Items`) %>%
arrange(desc(`Total Items`)) %>%
select(9, 12, 13, 14) %>%
rename(
"BNF Presentation Name" = 1,
"Unit of Measure" = 2,
"Total Quantity" = 3,
"Total Items" = 4
) %>%
mutate(
`Total Quantity` = formatC(
signif(`Total Quantity`, 3),
big.mark = ",",
format = "d"
),
`Total Items` = formatC(
signif(`Total Items`, 3),
big.mark = ",",
format = "d"
)
) %>%
DT::datatable(rownames = FALSE,
options = list(dom = "t",
columnDefs = list(
list(orderable = FALSE,
targets = "_all"),
list(className = "dt-left", targets = 0:1),
list(className = "dt-right",
targets = 2:3)
)))
```
Source: [Financial Year Summary Tables - Presentations table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
In 2021/22 Vagifem 10 microgram vaginal tablets had the most prescribed items with 589,000 total items. This is a 13.2% increase of 68,600 items since 2020/21.
### Table 3: Top 10 presentations by prescribed items in `r ltst_month_tidy` {.toc-ignore}
```{r top_pres_month, out.width="100%"}
presentation_monthly %>%
filter(`Year Month` == ltst_month) %>%
top_n(10, `Total Items`) %>%
arrange(desc(`Total Items`)) %>%
select(10, 13, 14, 15) %>%
rename(
"BNF Presentation Name" = 1,
"Unit of Measure" = 2,
"Total Quantity" = 3,
"Total Items" = 4
) %>%
mutate(
`Total Quantity` = formatC(
signif(`Total Quantity`, 3),
big.mark = ",",
format = "d"
),
`Total Items` = formatC(
signif(`Total Items`, 3),
big.mark = ",",
format = "d"
)
) %>%
DT::datatable(rownames = FALSE,
options = list(dom = "t",
columnDefs = list(
list(orderable = FALSE,
targets = "_all"),
list(className = "dt-left", targets = 0:1),
list(className = "dt-right",
targets = 2:3)
)))
```
In June 2022 Utrogestan 100mg capsules had the most prescribed items at 65,400 totals items.
Source: [Financial Year Summary Tables - Presentations table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
### Figure 6: Estimated number of identified patients per 1,000 population by Integrated Care Board (ICB) in `r ltst_year` {.toc-ignore}
```{r patients_per_1000, out.width = "100%"}
chart_data <- raw_data$icb_annual %>%
filter(
FINANCIAL_YEAR == ltst_year,
PATIENT_COUNT != 0
) %>%
left_join(
stp_pop,
by = c("STP_CODE" = "STP21CDH")
) %>%
#select(1, 4,5,7,12) %>%
mutate(
PER_1000 = round(PATIENT_COUNT / POP * 1000, 1)
) %>%
arrange(desc(PER_1000)) %>%
filter(
STP_NAME != "UNKNOWN STP"
)
chart <- basic_chart_hc(
chart_data,
x = STP_CODE,
y = PER_1000,
type = "column",
xLab = "ICB code",
yLab = "Patients per 1,000 population",
title = ""
) %>%
hc_tooltip(enabled = T,
useHTML = TRUE,
formatter = JS("function(){
var result = this.point.STP_NAME + '<br><b>Patients per 1,000 population:</b> ' + this.point.PER_1000.toFixed(0)
return result
}")) %>%
hc_yAxis(
labels = list(
enabled = T
)
)
chart$x$hc_opts$series[[1]]$dataLabels$enabled <- FALSE
chart
```
Source: [Financial Year Summary Tables - ICB table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
The figures in Figure 6 have been calculated by joining the [Office for National Statistics (ONS) Mid-2020 Population Estimates for 2021 Clinical Commissioning Groups in England](https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fpopulationandmigration%2fpopulationestimates%2fdatasets%2fclinicalcommissioninggroupmidyearpopulationestimates%2fmid2020sape23dt6a/sape23dt6amid2020ccg2021estimatesunformatted.xlsx) to our data to obtain the estimated population of each ICB.
In 2021/22 NHS North East London Integrated Care Board was the ICB with the lowest estimated number of identified patients who received at least one HRT item per 1,000 residents with an estimated 17 patients per 1,000 population.
NHS Dorset Integrated Care Board was the ICB with the highest estimated number of identified patients who received at least one HRT item per 1,000 residents in 2021/22 with an estimated 50 patients per 1,000 population.
### Figure 7: Estimated number of identified patients by age band in `r ltst_year` {.toc-ignore}
```{r ageband_fy, out.width="100%"}
chart_data <- raw_data$ageband_annual %>%
filter(
FINANCIAL_YEAR == ltst_year
) %>%
filter(PATIENT_COUNT != 0)
chart <- basic_chart_hc(
chart_data,
x = AGE_BAND,
y = PATIENT_COUNT,
type = "column",
xLab = "Age band",
yLab = "Patients",
title = ""
) %>%
hc_tooltip(enabled = F)
chart$x$hc_opts$series[[1]]$dataLabels$allowOverlap <- TRUE
chart
```
Source: [Financial Year Summary Tables - Age Band table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
The most common group to receive prescribing for HRT in 2021/22 was patients aged 50 to 54 with an estimated 437,000 identified patients, 22.7% of all patients. The next most common groups were patients aged 55 to 59 with 356,000 and 18.5% of identified patients, and patients 45 to 49 with 246,000 and 12.8% of identified patients.
More information on how we calculate a patient’s age can be found in section 4 of this summary.
### Figure 8: Estimated number of identified patients by IMD quintile in `r ltst_year` {.toc-ignore}
``` {r imd_fy, out.width = "100%"}
chart_data <- raw_data$quintile_annual %>%
filter(
FINANCIAL_YEAR == ltst_year
) %>%
filter(PATIENT_COUNT != 0,
!is.na(IMD_QUINTILE)) %>%
mutate(
IMD_QUINTILE = case_when(
IMD_QUINTILE == 1 ~ as.character("1 - most deprived"),
IMD_QUINTILE == 5 ~ as.character("5 - least deprived"),
TRUE ~ as.character(IMD_QUINTILE)
)
)
chart <- basic_chart_hc(
chart_data,
x = IMD_QUINTILE,
y = PATIENT_COUNT,
type = "column",
xLab = "IMD quintile",
yLab = "Patients",
title = ""
) %>%
hc_tooltip(enabled = F)
chart$x$hc_opts$series[[1]]$dataLabels$allowOverlap <- TRUE
chart
```
Source: [Financial Year Summary Tables - IMD Quintile table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
In 2021/22, there were an estimated 477,000 identified patients prescribed HRT medicines in the least deprived areas in England, 92.6% more than the 248,000 identified patients that received prescribing from in the more deprived areas. In general, more people were prescribed HRT medicines in the least deprived areas in 2021/22. This pattern has remained consistent since 2015/16.
The English Indices of Deprivation have been used to provide a measure of patient deprivation. The deprivation quintiles reported are those of the patient's registered address. More information on this measure can be found in section 4 of this summary.
### Figure 9: Proportion of patients under/over 60 by IMD quintile in `r ltst_year` {.toc-ignore}
``` {r imd_age, out.width = "100%"}
chart_data <- raw_data$quintile_age_annual %>%
filter(
FINANCIAL_YEAR == ltst_year
) %>%
filter(PATIENT_COUNT != 0,
!is.na(IMD_QUINTILE),
AGE_BAND != "Unknown") %>%
mutate(
GROUP = case_when(
AGE_BAND <= "55-59" ~ "Under 60",
TRUE ~ "Over 60"
)
) %>%
group_by(
GROUP,
IMD_QUINTILE
) %>%
summarise(
PATIENT_COUNT = sum(PATIENT_COUNT),
.groups = "drop"
) %>%
group_by(
IMD_QUINTILE
) %>%
mutate(
PROP = PATIENT_COUNT / sum(PATIENT_COUNT) * 100
) %>%
ungroup() %>%
mutate(
IMD_QUINTILE = case_when(
IMD_QUINTILE == 1 ~ as.character("1 - most deprived"),
IMD_QUINTILE == 5 ~ as.character("5 - least deprived"),
TRUE ~ as.character(IMD_QUINTILE)
)
) %>%
arrange(
desc(GROUP)
)
group_chart_hc(
chart_data,
x = IMD_QUINTILE,
y = PROP,
group = GROUP,
type = "column",
marker = FALSE,
dlOn = FALSE,
xLab = "IMD quintile",
yLab = "Proportion (%)",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE) %>%
hc_tooltip(
valueDecimals = 1,
valueSuffix = "%"
)%>%
hc_plotOptions(
column = list(
stacking = "normal"
)
) %>%
hc_colors(c("#ED8B00", "#005EB8"))
```
Source: [Financial Year Summary Tables - IMD Quintile Age table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
In 2021/22 there were an estimated 190,000 identified patients under the age of 60 in the most deprived areas who were prescribed at least one HRT item. This was 76.6% of the total identified patients in the most deprived areas. In the least deprived areas 308,000 identified patients were under 60. This was 64.6% of the total identified patients in the least deprived area who were prescribed at least one HRT item.
### Figure 10: Proportion of chargeable items by financial year {.toc-ignore}
``` {r charge_fy, out.width = "100%"}
chart_data <- raw_data$exempt_annual %>%
dplyr::mutate(
FINANCIAL_YEAR = case_when(
FINANCIAL_YEAR == max(FINANCIAL_YEAR) ~ paste0(FINANCIAL_YEAR, "<br>(YTD ", ltst_month_tidy, ")"),
TRUE ~ FINANCIAL_YEAR
)
) %>%
filter(
CHARGE_STATUS != "Null Charge Status"
) %>%
mutate(
CHARGE_STATUS = case_when(
EXEMPT_CAT == "Pre-Payment Certificate" ~ "Used PPC",
CHARGE_STATUS %in% c("Chargeable at Current Rate", "Chargeable at Previous Rate") ~ "Charged",
TRUE ~ CHARGE_STATUS
)
) %>%
group_by(
FINANCIAL_YEAR,
CHARGE_STATUS
) %>%
summarise(
ITEM_COUNT = sum(ITEM_COUNT, na.rm = T),
.groups = "drop"
) %>%
group_by(
FINANCIAL_YEAR
) %>%
mutate(
PROP = ITEM_COUNT / sum(ITEM_COUNT, na.rm = T) * 100
) %>%
ungroup()
group_chart_hc(
chart_data,
x = FINANCIAL_YEAR,
y = PROP,
group = CHARGE_STATUS,
type = "column",
marker = FALSE,
dlOn = FALSE,
xLab = "Financial year",
yLab = "Proportion (%)",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE) %>%
hc_tooltip(
valueDecimals = 1,
valueSuffix = "%"
) %>%
hc_plotOptions(
column = list(
stacking = "normal"
)
)%>%
hc_colors(c("#ED8B00", "#003087", "#41B6E6"))%>%
hc_yAxis(gridLineColor = "#e6e6e6")
```
Source: [Financial Year Summary Tables - Exemption Categories table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_financial_year_2022_2023_YTD_June_2022_v001.xlsx)
In England, most working-age adults have to pay prescription charges and any items dispensed to this group are counted as chargeable. Certain groups are exempt from paying for their NHS prescriptions, for example those over the age of 60, and some prescribed items are always free, including contraceptives. If a patient regularly pays prescription charges, a Prescription Pre-Payment certificate (PPC) could save them money as they can pay one up front cost either annually or for a 3 month period.
The proportion of HRT drug items which were prescribed to patients who were using a PPC showed a steady increase from 10.2% in 2015/16 to 15.0% in 2020/21. There was a 3.45 percentage point decrease in the proportion of HRT drug items prescribed to patients using a PPC to 11.5% in 2021/22. This trend has continued into 2022/23 where there was a further decrease of 1.83 percentage points to 9.70% in the period April 2022 to June 2023. This is consistent with overall prescribing where the proportion of items prescribed to patients using a PPC has been declining since 2019/20.
### Figure 11: Proportion of chargeable items by month July 2020 to June 2022 {.toc-ignore}
``` {r exempt_monthly, out.width = "100%"}
chart_data <- raw_data$exempt_monthly %>%
filter(
YEAR_MONTH >= lt_st_month_min
) %>%
filter(
CHARGE_STATUS != "Null Charge Status"
) %>%
mutate(
CHARGE_STATUS = case_when(
EXEMPT_CAT == "Pre-Payment Certificate" ~ "Used PPC",
CHARGE_STATUS %in% c("Chargeable at Current Rate", "Chargeable at Previous Rate") ~ "Charged",
TRUE ~ CHARGE_STATUS
)
) %>%
group_by(
YEAR_MONTH,
CHARGE_STATUS
) %>%
summarise(
ITEM_COUNT = sum(ITEM_COUNT, na.rm = T),
.groups = "drop"
) %>%
group_by(
YEAR_MONTH
) %>%
mutate(
PROP = ITEM_COUNT / sum(ITEM_COUNT, na.rm = T) * 100
) %>%
ungroup() %>%
mutate(
YEAR_MONTH = base::as.Date(as.character(paste0(YEAR_MONTH,"01")), format = "%Y%m%d")
)
group_chart_hc(
chart_data,
x = YEAR_MONTH,
y = PROP,
group = CHARGE_STATUS,
type = "column",
marker = FALSE,
dlOn = FALSE,
xLab = "Month",
yLab = "Proportion (%)",
title = ""
) %>%
hc_tooltip(enabled = TRUE,
shared = TRUE,
sort = TRUE) %>%
hc_legend(enabled = TRUE) %>%
hc_tooltip(
valueDecimals = 1,
valueSuffix = "%"
) %>%
hc_plotOptions(
column = list(
stacking = "normal"
)
) %>%
hc_xAxis(
type = "datetime"
) %>%
hc_colors(c("#ED8B00", "#003087", "#41B6E6"))%>%
hc_yAxis(gridLineColor = "#e6e6e6")
```
Source: [Monthly Summary Tables - Exemption Categories table](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt_monthly_June_2022_v001.xlsx)
In the period July 2020 to June 2022 there has been a steady increase in the proportion of HRT drug items which have been prescribed to patients who paid in full for their prescriptions. This has increased by 4.45 percentage points from 26.2% in July 2020 to 30.6% in June 2022. This differs from the pattern seen in overall prescribing where the proportion of items prescribed to patients who paid in full for their prescriptions has been consistent since July 2020.
There has been a steady decline in the proportion of HRT drug items prescribed to a patient using a PPC in the period July 2020 to June 2022. This decreased by 5.63 percentage points from 15.1% in July 2020 to 9.49% in June 2022. This is consistent with overall prescribing where the proportion of items prescribed to patients using a PPC has been declining since July 2020.
## 3. Background
The menopause is a biological stage in a woman's life that occurs when she stops menstruating and reaches the end of her natural reproductive life. This is not usually abrupt, but a gradual process during which women experience perimenopause before reaching postmenopause. Usually the menopause is defined as having occurred when a woman has not had a period for 12 consecutive months (for women reaching menopause naturally).
As well as a change in their menstrual cycle women may experience a variety of symptoms associated with menopause such as hot flushes and sweats, low mood, vaginal dryness, low sexual desire, and others.
Most women will experience some symptoms, symptoms can last for months or years, and can change with time. Prolonged lack of the hormone oestrogen affects the bones and cardiovascular system, and postmenopausal women are at increased risk of a number of long-term conditions, such as osteoporosis.
HRT, which replaces the hormones that are at low levels, is the main medicine treatment for menopause and perimenopause symptoms. More information on the menopause is available on the [NHS website](https://www.nhs.uk/conditions/menopause/).
### 3.1. Female sex hormones and their modulators {.toc-ignore}
Oestrogen-only HRT is usually only recommended for women who have had their womb removed during a hysterectomy. For women with a uterus, oestrogen plus progestogen HRT is recommended. A progestogen should be prescribed to reduce the risk of cystic hyperplasia of the endometrium, a thickening of the endometrium, and possible transformation to cancer.
HRT comes in several different forms such as tablets and skin patches. Treatment routine for HRT depends on whether the patient is in the early stages of the menopause or have had menopausal symptoms for some time. More information on the types and routines of HRT treatment can be found on the [NHS website](https://www.nhs.uk/conditions/hormone-replacement-therapy-hrt/types/).
The 2 types of routines are cyclical (or sequential) HRT and continuous combined HRT.
Continuous combined preparations are not suitable for use in the perimenopause or within 12 months of the last menstrual period; women who use such preparations may bleed irregularly in the early stages of treatment.
You can find out more information on [sex hormones](https://bnf.nice.org.uk/treatment-summaries/sex-hormones/) on the NICE website.
### 3.2. Preparations for vaginal and vulval changes {.toc-ignore}
Preparations for vaginal and vulval changes are used to treat the symptoms of vaginal atrophy related to oestrogen deficiencies related to the menopause.
There are non-hormonal vaginal moisturisers available. Menopausal women with vaginal dryness can use vaginal moisturisers and lubricants alone or in addition to vaginal oestrogen.
You can find out more information on [vaginal and vulval conditions](https://bnf.nice.org.uk/treatment-summaries/vaginal-and-vulval-conditions/) on the NICE website.
### 3.3. Gina 10 microgram vaginal tablets {.toc-ignore}
Pharmacists in the UK can now dispense the hormone replacement therapy (HRT) drug Gina 10 microgram vaginal tablets without a prescription.
Gina 10 microgram vaginal tablets are a locally acting oestrogen HRT and the only treatment available without a prescription that is clinically proven to target and treat the cause of vaginal atrophy and its associated symptoms after the menopause.
Gina 10 microgram vaginal tablets are a proprietary equivalent of Estradiol 10 microgram pessaries. The NHSBSA does not collect information about medicines that are sold over the counter, therefore these statistics may not be representative of the whole population taking HRT
### 3.4. Shortages {.toc-ignore}
There have been concerns that some women across the UK were unable to obtain their prescriptions due to shortages of HRT products. This coupled with the impact of COVID-related global supply problems means that many women are extremely concerned about not being able to get access to this vital medication.
There are over 70 HRT products available in the UK and while most remain in good supply, a range of factors including an increase in demand has led to supply issues with a limited number of products. The Department of Health and Social Care (DHSC) continues to work closely with suppliers and other stakeholders to maintain overall HRT supply to patients across the UK. This includes working to expedite resupply dates of the disrupted HRT products to resolve issues as soon as possible and to help fill supply gaps and prevent future shortages. Access to in-demand products has improved since Serious Shortage Protocols (SSPs) were issued to limit dispensing to three months’ supply to even out distribution and allow alternative products to be dispensed, as necessary. Suppliers have taken swift action to secure additional stock of HRT products which are experiencing shortages and deliveries of further stock have been expedited. These actions have increased near-term supply and the supply position has improved. To support continued growth in the longer term, suppliers are taking steps to further build capacity and increase production.
## 4. About these statistics
This publication is a one-off ad-hoc publication by the NHSBSA. To learn more about how this publication compares to our other publications, you can view the Official Statistics guidance table on our [statistical collections page](https://www.nhsbsa.nhs.uk/statistical-collections).
Further information on the methodology used in this publication and further background information is available in our [Background Information and Methodology](https://nhsbsa-opendata.s3.eu-west-2.amazonaws.com/hrt/hrt-background-info-methodology-v001.html) 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 patient 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 HRT in January 2021, and another in February 2021 then adding together those totals would count that patient twice. For the same reason, data on patient counts for different BNF sections should not be added together.
### 4.2. Patient age {.toc-ignore}
The age 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. Averages {.toc-ignore}
Where this document refers to 'average', this is the mean unless otherwise stated. This is calculated by adding the number of items/patients/months together and dividing this by the number of items/patients/months.
### 4.4. 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.