Time periods
Listing time periods
The CVD Prevent audit outputs data around four times a year. To see
which are the four latest available periods for standard indicators you
can use the function cvd_time_period_list():
cvd_time_period_list() |>
dplyr::filter(IndicatorTypeName == 'Standard') |>
dplyr::slice_max(order_by = TimePeriodID, n = 4) |>
dplyr::select(TimePeriodID, TimePeriodName) |>
gt::gt()| TimePeriodID | TimePeriodName |
|---|---|
| 17 | To March 2024 |
| 17 | To March 2024 |
| 15 | To December 2023 |
| 10 | To September 2023 |
There are two main types of indicator reported; ‘Standard’ (ID1) and
‘Outcomes’ (ID2). Pass in an ID value for the optional parameter,
indicator_type_id to get a list of time periods for which
the indicator type was reported.
cvd_time_period_list(indicator_type_id = 2) |>
dplyr::slice_max(order_by = TimePeriodID, n = 4) |>
dplyr::select(IndicatorTypeID, IndicatorTypeName, TimePeriodID, TimePeriodName) |>
gt::gt()| IndicatorTypeID | IndicatorTypeName | TimePeriodID | TimePeriodName |
|---|---|---|---|
| 2 | Outcomes | 16 | Jan 2023 - Dec 2023 |
| 2 | Outcomes | 14 | Jul 2022 - Jun 2023 |
| 2 | Outcomes | 13 | Oct 2022 - Sep 2023 |
| 2 | Outcomes | 12 | Apr 2022 - Mar 2023 |
Listing system levels per period
Data can be reported at different geographies (aka System Levels) in
each period; typically ranging from individual GP practices to national
level (England). To get the available system levels we can use the
cvd_time_period_system_levels() function.
cvd_time_period_system_levels() |>
dplyr::slice_max(order_by = TimePeriodID) |>
dplyr::select(TimePeriodID, TimePeriodName, SystemLevelID, SystemLevelName) |>
gt::gt()| TimePeriodID | TimePeriodName | SystemLevelID | SystemLevelName |
|---|---|---|---|
| 17 | To March 2024 | 1 | England |
| 17 | To March 2024 | 4 | PCN |
| 17 | To March 2024 | 5 | Practice |
| 17 | To March 2024 | 6 | Region |
| 17 | To March 2024 | 7 | ICB |
| 17 | To March 2024 | 8 | Sub-ICB |
Areas
List available system levels for a given time period
To find out which system levels are reported for a specific time
period you can use the cvd_area_system_level() function,
providing the required time_period_id, (NB, time_period_id defaults to
the first time period if not supplied).
cvd_area_system_level(time_period_id = 17) |>
dplyr::select(SystemLevelID, SystemLevelName) |>
gt::gt()| SystemLevelID | SystemLevelName |
|---|---|
| 1 | England |
| 6 | Region |
| 7 | ICB |
| 8 | Sub-ICB |
| 4 | PCN |
| 5 | Practice |
List all available reporting periods for each system level
Use the cvd_area_system_level_time_periods() function to
show a list of all reporting periods for each system level. Here we show
the latest four reporting periods at the GP practice system level.
cvd_area_system_level_time_periods() |>
dplyr::filter(SystemLevelName == 'Practice') |>
dplyr::slice_max(order_by = TimePeriodID, n = 4) |>
dplyr::select(SystemLevelID, SystemLevelName, TimePeriodID, TimePeriodName) |>
gt::gt()| SystemLevelID | SystemLevelName | TimePeriodID | TimePeriodName |
|---|---|---|---|
| 5 | Practice | 17 | To March 2024 |
| 5 | Practice | 15 | To December 2023 |
| 5 | Practice | 10 | To September 2023 |
| 5 | Practice | 9 | To June 2023 |
List areas for a time period and system level or parent area
To list four of the Primary Care Networks (PCN) (SystemLevelID = 4)
for which data is available at time period 17 use the
cvd_area_list() function.
cvd_area_list(time_period_id = 17, system_level_id = 4) |>
dplyr::select(SystemLevelName, AreaID, AreaCode, AreaName) |>
dplyr::slice_head(n = 4) |>
gt::gt()| SystemLevelName | AreaID | AreaCode | AreaName |
|---|---|---|---|
| PCN | 1103 | U60176 | 3 Centres PCN |
| PCN | 1103 | U60176 | 3 Centres PCN |
| PCN | 920 | U72999 | 4 Doncaster PCN |
| PCN | 920 | U72999 | 4 Doncaster PCN |
Either parent area or system level must be specified.
View details for a specific area
To view details for a specific area use
cvd_area_details() with the required parameters of
‘time_period_id’ and ‘area_id’. The return from this function is a list
of three named tibbles:
area_details,area_child_details(where appropriate), andarea_parent_details(where appropriate).
# get the list from the function
returned_list <- cvd_area_details(time_period_id = 17, area_id = 1103)The tibbles first need extracting from the returned list object, done
here using the list$object notation. To view area
details:
| AreaCode | AreaName | SystemLevelID |
|---|---|---|
| U60176 | 3 Centres PCN | 4 |
View details for the parent of this area:
| AreaID | AreaName | SystemLevelID |
|---|---|---|
| 7959 | NHS West Yorkshire ICB - X2C4Y | 8 |
View details for the children of this area:
| AreaID | AreaName | SystemLevelID |
|---|---|---|
| 1929 | Calder View Surgery | 5 |
| 1932 | North Road Suite,Ravensthorpe Health Ctr | 5 |
| 1938 | Mirfield Health Centre | 5 |
| 1939 | Eightlands Surgery | 5 |
| 1978 | Dr Mahmood & Partners | 5 |
List areas without parent details
Some areas do not have parent details but do have data reported in a given period, which may mean they are missed when searching for areas. This function provides a convenient way of accessing these details for a given ‘time_period_id’ and ‘system_level_id’.
Here we report four GP practices without any parent PCN:
cvd_area_unassigned(time_period_id = 17, system_level_id = 5) |>
dplyr::slice_head(n = 4) |>
dplyr::select(SystemLevelName, AreaID, AreaName) |>
gt::gt()| SystemLevelName | AreaID | AreaName |
|---|---|---|
| Practice | 4877 | 27@Northgate |
| Practice | 2569 | Beauchief Medical Practice |
| Practice | 2040 | Bramley Village Health & Wellbeing Ctr |
| Practice | 5680 | Central Clinic |
The top system_level (England) does not have a parent either:
cvd_area_unassigned(time_period_id = 17, system_level_id = 1) |>
dplyr::select(SystemLevelName, AreaID, AreaName) |>
gt::gt()| SystemLevelName | AreaID | AreaName |
|---|---|---|
| England | 1 | England |
Search for area by keyword
To find details for an area where you don’t know its ID number you
can perform a partial name search using cvd_area_search()
and specify a time period to search.
cvd_area_search(partial_area_name = 'foo', time_period_id = 17) |>
dplyr::select(AreaID, AreaName, AreaCode) |>
gt::gt()| AreaID | AreaName | AreaCode |
|---|---|---|
| 7327 | HILLFOOT HEALTH | N82116 |
| 1991 | Hillfoot Surgery | B86011 |
| 6155 | Waterfoot Medical Practice | P81132 |
Area details
Nested
To get details for areas, including all system areas within the area,
you can use the function cvd_area_nested_subsystems() . An
area ID is a required parameter. Here we request details for North West
NHS Region which returns a named list:
return_list <- cvd_area_nested_subsystems(area_id = 7665)
return_list |> summary()
#> Length Class Mode
#> level_1 7 tbl_df list
#> level_2 7 tbl_df list
#> level_3 7 tbl_df list
#> level_4 7 tbl_df list
#> level_5 7 tbl_df listThe named tibble level_1 provides details about our
requested area, which can be accessed using the
return_list$indicators:
return_list$level_1 |>
gt::gt()| AreaCode | AreaID | AreaName | AreaOdsCode | ParentAreaID | SystemLevelID | SystemLevelName |
|---|---|---|---|---|---|---|
| E40000010 | 7665 | North West | Y62 | 1 | 6 | Region |
The next level down are areas that are children of the region:
return_list$level_2 |>
gt::gt()| AreaCode | AreaID | AreaName | AreaOdsCode | ParentAreaID | SystemLevelID | SystemLevelName |
|---|---|---|---|---|---|---|
| E54000007 | 33 | Greater Manchester Health and Social Care Partnership | NA | 7665 | 2 | STP |
| E54000008 | 37 | Cheshire and Merseyside | QYG | 7665 | 2 | STP |
| E54000048 | 42 | Healthier Lancashire and South Cumbria | QE1 | 7665 | 2 | STP |
| E54000057 | 8030 | NHS Greater Manchester Integrated Care Board | QOP | 7665 | 7 | ICB |
| E54000048 | 8034 | NHS Lancashire and South Cumbria Integrated Care Board | QE1 | 7665 | 7 | ICB |
| E54000008 | 8068 | NHS Cheshire and Merseyside Integrated Care Board | QYG | 7665 | 7 | ICB |
The next level down are the children of these areas … and so on.
Flat
Alternatively, you can request a flat output grouped on system level
using the cvd_area_flat_subsystem() function:
cvd_area_flat_subsystems(area_id = 5) |>
dplyr::glimpse()
#> Rows: 75
#> Columns: 14
#> $ AreaCode <chr> "E54000038", "E54000038", "E54000038", "E54…
#> $ AreaID <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
#> $ AreaName <chr> "Somerset", "Somerset", "Somerset", "Somers…
#> $ AreaOdsCode <chr> "QSL", "QSL", "QSL", "QSL", "QSL", "QSL", "…
#> $ ParentAreaID <int> 7670, 7670, 7670, 7670, 7670, 7670, 7670, 7…
#> $ SystemLevelID <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
#> $ SystemLevelName <chr> "STP", "STP", "STP", "STP", "STP", "STP", "…
#> $ SubSystems_AreaCode <chr> "E38000150", "U47425", "U17153", "U84175", …
#> $ SubSystems_AreaID <int> 105, 528, 613, 677, 688, 743, 786, 868, 963…
#> $ SubSystems_AreaName <chr> "NHS Somerset CCG", "West Somerset PCN", "W…
#> $ SubSystems_AreaOdsCode <chr> "11X", NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ SubSystems_ParentAreaID <int> 5, 105, 105, 105, 105, 105, 105, 105, 105, …
#> $ SubSystems_SystemLevelID <int> 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5…
#> $ SubSystems_SystemLevelName <chr> "CCG", "PCN", "PCN", "PCN", "PCN", "PCN", "…Indicators
An Indicator represents a cardiovascular disease health indicator as defined by NHS England. An example of an Indicator is CVDP001AF, ‘Prevalence of GP recorded atrial fibrillation in patients aged 18 and over’. Indicators have unique Indicator IDs. Each indicator is further broken down into Metrics.
A Metric represents a further breakdown of an indicator by inequality markers. An example of an inequality marker is ‘Age Group - Male, 40-59’. Metrics have unique Metric IDs with each representing a combination of Indicator and Metric Category.
A Metric Category describes the inequality markers which the Metric applies to. Each Metric Category has a unique ID for each combination of Name and Metric Category Type.
A Metric Category belongs to a Metric Category Type which groups the Metric Categories into one entity. Each Metric Category Type has a unique ID.
For example, ‘Male - 40-59’ is a Metric Category in the ‘Age Group’ Metric Category Type. Age Group will also contain categories for ‘18-39’, ‘40-59’, ‘60-79’, ‘80+’. Assigning Metric Categories to Metric Category Type allows comparison of all metrics in one inequality marker (in this case Age Group), or displaying Metric Categories in the same Metric Category Type alongside each other.
Listing indicators
To get a list of all available indicators for a given time period and
system level you can use the cvd_indicator_list() function
and specify which time period and system level you are interested
in.
Here we access the first four indicators for time point 17 and GP practice level (system level 5).
cvd_indicator_list(time_period_id = 17, system_level_id = 5) |>
dplyr::select(IndicatorID, IndicatorCode, IndicatorShortName) |>
dplyr::slice_head(n = 4) |>
gt::gt()| IndicatorID | IndicatorCode | IndicatorShortName |
|---|---|---|
| 13 | CVDP002CKD | CKD: uncoded case finder |
| 20 | CVDP005HYP | Hypertension: high risk case finder |
| 23 | CVDP010CHOL | Cholesterol: CKD treated with LLT |
| 24 | CVDP001SMOK | Smoking: record of smoking status |
Listing metrics for each indicator
To list all metrics for each indicator you can use the
cvd_indicator_metric_list() function and specify which time
period and system level you are interested in.
Here we access the metrics for the prevalence of atrial fibrillation (Indicator ID 1) and focussing on just those metrics available for the 40-59 years age group:
cvd_indicator_metric_list(time_period_id = 17, system_level_id = 1) |>
dplyr::filter(IndicatorID == 1, MetricCategoryName == '40-59') |>
dplyr::count(IndicatorID, IndicatorShortName, MetricID, MetricCategoryName, CategoryAttribute) |>
dplyr::select(-n) |>
gt::gt()| IndicatorID | IndicatorShortName | MetricID | MetricCategoryName | CategoryAttribute |
|---|---|---|---|---|
| 1 | AF: prevalence | 173 | 40-59 | Female |
| 1 | AF: prevalence | 175 | 40-59 | Male |
| 1 | AF: prevalence | 187 | 40-59 | Persons |
List all indicator data for a given area
To access all indicator data for a given area and time period you can
use the cvd_indicator() function. The return from the API
includes four sets of data which are grouped together as four tibbles
within a named list object. The four tibbles include:
indicators,
metric categories,
metric data,
time-series data,
Here we look at ‘3 Centres PCN’ (area ID 1103) for time period 17:
return_list <- cvd_indicator(time_period_id = 17, area_id = 1103)
return_list |> summary()
#> Length Class Mode
#> indicators 14 tbl_df list
#> metric_categories 7 tbl_df list
#> metric_data 19 tbl_df list
#> timeseries_data 7 tbl_df listIndicators
We can extract the tibbles using the list$object notation. To
illustrate, we obtain the first four indicators from the
return_list$indicators list item:
return_list$indicators |>
dplyr::select(IndicatorID, IndicatorCode, IndicatorShortName) |>
dplyr::arrange(IndicatorID) |>
dplyr::slice_head(n = 4) |>
gt::gt()| IndicatorID | IndicatorCode | IndicatorShortName |
|---|---|---|
| 2 | CVDP002HYP | Hypertension: treatment to recommended age specific (79 and under) threshold |
| 3 | CVDP003HYP | Hypertension: treatment to recommended age specific (80 and over) threshold |
| 4 | CVDP004HYP | Hypertension: BP monitoring |
| 7 | CVDP002AF | AF: treatment with anticoagulants |
Category
Here we access category details for:
indicator 7: (AF: treatment with anticoagulants)
metric categories 7 & 8 (people aged 40-59 years by gender)
return_list$metric_categories |>
dplyr::filter(IndicatorID == 7, MetricCategoryID %in% c(7, 8)) |>
dplyr::select(IndicatorID, MetricCategoryTypeName,
CategoryAttribute, MetricCategoryName, MetricID) |>
gt::gt()| IndicatorID | MetricCategoryTypeName | CategoryAttribute | MetricCategoryName | MetricID |
|---|---|---|---|---|
| 7 | Age group | Male | 40-59 | 126 |
| 7 | Age group | Female | 40-59 | 132 |
Category data
Here we access the data for each of the above categories:
return_list$metric_data |>
dplyr::filter(MetricID %in% c(126, 132)) |>
dplyr::select(MetricID, Value, Numerator, Denominator) |>
gt::gt()| MetricID | Value | Numerator | Denominator |
|---|---|---|---|
| 126 | 100 | 15 | 15 |
| 132 | 100 | 10 | 10 |
Time series
A set of time-series data area also supplied for returned metrics,
which provide results for all available time periods. This data is
accessible from the timeseries_data object.
Here we show the time-series for the two categories of metric shown above as a plot.
return_list$timeseries_data |>
dplyr::filter(MetricID %in% c(126, 132), !is.na(Value)) |>
# prepare for plotting
dplyr::mutate(
EndDate = as.Date(EndDate, format = '%a, %d %b %Y 00:00:00 GMT'),
MetricID = MetricID |> as.factor()
) |>
ggplot2::ggplot(ggplot2::aes(
x = EndDate,
y = Value,
group = MetricID ,
colour = MetricID)) +
ggplot2::geom_point() +
ggplot2::geom_line()
Tags
Indicators are searchable by one or more Tag, supplied by the optional argument tag_id. Tag ID can be a single ID number or a vector or IDs, shown here as a vector of IDs 12 and 13 (hypertension and blood pressure measures).
return_list <-
cvd_indicator(time_period_id = 17, area_id = 3, tag_id = c(3, 4))
return_list$indicators |>
dplyr::select(IndicatorID, IndicatorCode, IndicatorShortName) |>
dplyr::arrange(IndicatorID) |>
dplyr::slice_head(n = 4) |>
gt::gt()| IndicatorID | IndicatorCode | IndicatorShortName |
|---|---|---|
| 2 | CVDP002HYP | Hypertension: treatment to recommended age specific (79 and under) threshold |
| 3 | CVDP003HYP | Hypertension: treatment to recommended age specific (80 and over) threshold |
| 4 | CVDP004HYP | Hypertension: BP monitoring |
| 11 | CVDP001HYP | Hypertension: prevalence |
See also List indicator tags.
List indicator tags
Indicators have one or more tags that can be used to filter related
indicators. To get a list of these tags you can use the
cvd_indicator_tags() function
cvd_indicator_tags() |>
dplyr::arrange(IndicatorTagID) |>
dplyr::slice_head(n = 5) |>
gt::gt()| IndicatorTagID | IndicatorTagName |
|---|---|
| 1 | prevalence |
| 2 | atrial fibrillation |
| 3 | hypertension |
| 4 | blood pressure measures |
| 5 | cardiovascular disease |
Get meta-data for an indicator
To get details and meta-data for a specific indicator, use the
cvd_indicator_details() function, specifying the indicator
ID of interest. Meta-data includes details such as copyright
information, source of the data, definitions and method of calculating
confidence intervals.
cvd_indicator_details(indicator_id = 7) |>
dplyr::select(IndicatorID, MetaDataTitle, MetaData) |>
dplyr::slice_head(n=5) |>
gt::gt()| IndicatorID | MetaDataTitle | MetaData |
|---|---|---|
| 7 | Copyright | Produced by Office for Health Improvement and Disparities, and NHS Benchmarking Network. Source: Cardiovascular Disease Prevention Audit (CVDPREVENT), NHS Digital Copyright © 2021, Re-used with the permission of the NHS Digital. All rights reserved |
| 7 | Data source | Cardiovascular Disease Prevention Audit (CVDPREVENT) |
| 7 | Definition | The percentage of patients aged 18 and over with GP recorded atrial fibrillation and a record of a CHA2DS2-VASc score of 2 or more who are currently treated with anticoagulation drug therapy |
| 7 | Indicator title | Patients with high risk atrial fibrillation who are receiving anticoagulation therapy |
| 7 | Purpose/rationale | Atrial fibrillation (AF) is a major risk factor for stroke and contributes to one in five strokes. Strokes where AF is a contributory factor are often more severe with higher mortality and greater disability. For people with AF who are at most risk of stroke in most cases the benefits of anticoagulation significantly outweigh the risks of bleeding. https://www.stroke.org.uk/professionals/atrial-fibrillation-information-and-resources |
Sibling area data
To find performance for sibling areas (areas that share a common
parent area, such as GP practices within a PCN or PCNs within an ICB),
you can use the cvd_indicator_sibling() function,
specifying a time period, area and metric.
Here we get data for Metric 126 (relating to indicator 2- Hypertension treatment) for siblings to ‘3 Centres PCN’ (ID 1103):
cvd_indicator_sibling(time_period_id = 17, area_id = 1103, metric_id = 126) |>
dplyr::select(AreaID, AreaName, Value, LowerConfidenceLimit, UpperConfidenceLimit) |>
gt::gt()| AreaID | AreaName | Value | LowerConfidenceLimit | UpperConfidenceLimit |
|---|---|---|---|---|
| 1103 | 3 Centres PCN | 100.00 | 84.21 | 100.00 |
| 1023 | The Valleys Health & Social Care PCN | 93.75 | 73.68 | 100.00 |
| 315 | Tolson Care Partnership PCN | 90.91 | 64.29 | 100.00 |
| 709 | Spen Health & Wellbeing PCN | 90.91 | 72.00 | 96.00 |
| 523 | Dewsbury & Thornhill PCN | 88.89 | 58.33 | 100.00 |
| 607 | The Mast PCN | 88.89 | 58.33 | 100.00 |
| 1055 | Greenwood PCN | 86.67 | 61.11 | 94.44 |
| 606 | Batley Birstall PCN | 86.67 | 61.11 | 94.44 |
| 1334 | Viaduct Care PCN | 69.23 | 43.75 | 87.50 |
Child area data
To find performance for child areas (areas that are organisationally
one level lower than the specified area), you can use the
cvd_indicator_child_data() function, specifying a time
period, area and metric.
Here we get data for Metric 126 (relating to indicator 17 - Hypertension treatment) for the children of ‘NHS Shropshire, Telford and Wrekin CCG’ (ID 126):
cvd_indicator_child_data(time_period_id = 17, area_id = 74, metric_id = 126) |>
dplyr::select(AreaID, AreaName, Value, LowerConfidenceLimit, UpperConfidenceLimit) |>
gt::gt()| AreaID | AreaName | Value | LowerConfidenceLimit | UpperConfidenceLimit |
|---|---|---|---|---|
| 226 | Wrekin PCN | 100.00 | 80.00 | 100.00 |
| 899 | Se Shropshire PCN | 100.00 | 88.00 | 100.00 |
| 435 | Newport And Central PCN | 93.33 | 72.22 | 100.00 |
| 1373 | Shrewsbury PCN | 92.31 | 75.86 | 96.55 |
| 1201 | North Shropshire PCN | 91.30 | 73.08 | 96.15 |
| 848 | Sw Shropshire PCN | 90.91 | 64.29 | 100.00 |
| 1338 | Teldoc PCN | 80.00 | 60.71 | 89.29 |
| 731 | South East Telford PCN | 77.78 | 57.14 | 90.48 |
All indicator data for a given time and area
Use function cvd_indicator_data() along with the
indicator ID and specifying the time period and area to get all metric
data. The return includes national level data as comparison.
Here we look at ‘AF: treatment with anticoagulants’ (indicator ID 7) in time period 17 for ‘Leicester Central PCN’ (area_id 701) focussed on metrics by gender:
cvd_indicator_data(time_period_id = 17, indicator_id = 7, area_id = 701) |>
dplyr::filter(MetricCategoryTypeName == 'Sex') |>
dplyr::select(MetricID, MetricCategoryName, AreaData.AreaName, AreaData.Value, NationalData.AreaName, NationalData.Value) |>
gt::gt()| MetricID | MetricCategoryName | AreaData.AreaName | AreaData.Value | NationalData.AreaName | NationalData.Value |
|---|---|---|---|---|---|
| 150 | Persons | Leicester Central PCN | 95.40 | England | 91.48 |
| 125 | Female | Leicester Central PCN | 94.67 | England | 91.17 |
| 124 | Male | Leicester Central PCN | 95.96 | England | 91.74 |
All metric data for a given time and area
Use function cvd_indicator_metric_data() along with the
metric ID and specifying the time period and area to get all data. The
return includes national level data as comparison.
Here we retrieve metric data for AF: treatment with anticoagulants for ‘Alliance PCN’ (area ID 399) in time period 1 for metric 126 (breakdown by age group- males aged 40-59):
cvd_indicator_metric_data(metric_id = 126, time_period_id = 1, area_id = 399) |>
dplyr::select(IndicatorShortName, CategoryAttribute, MetricCategoryName, AreaData.Value, NationalData.Value) |>
gt::gt()| IndicatorShortName | CategoryAttribute | MetricCategoryName | AreaData.Value | NationalData.Value |
|---|---|---|---|---|
| AF: treatment with anticoagulants | Male | 40-59 | NA | 82.5 |
All indicator metric data for system level and time period
Perhaps one of the most useful functions when looking to do a
cross-sectional system-wide analysis,
cvd_indicator_raw_data() returns all metric data for a
specified indicator system level and time period.
Here we return all metric data for indicator ‘AF: treatment with anticoagulants’ (indicator ID 7) in time period 17 at GP practice level (system level ID 5):
cvd_indicator_raw_data(indicator_id = 7, time_period_id = 17, system_level_id = 5) |>
dplyr::slice_head(n = 5) |>
dplyr::select(AreaCode, AreaName, Value) |>
gt::gt()| AreaCode | AreaName | Value |
|---|---|---|
| P87657 | (IRLAM) SALFORD CARE CTRS MEDICAL PRACTI | 97.01 |
| P87620 | 1/Monton Medical Practice | 92.09 |
| P87004 | 1/SALFORD MEDICAL PRACTICE | 97.37 |
| N84035 | 15 Sefton Road | 87.85 |
| L81051 | 168 Medical Group | 91.84 |
Benchmark metric data with national results
To return metric data for a given area and time period along with
national results to compare against use function
cvd_indicator_nationalarea_metric_data().
Here we compare performance against metric 150 (AF: treatment with anticoagulants - all people) in ‘Chester South PCN’ (area ID 553) with national performance:
cvd_indicator_nationalarea_metric_data(metric_id = 150, time_period_id = 17, area_id = 553) |>
gt::gt()| AreaCode | AreaID | AreaName | NationalLevel | Value | TargetData |
|---|---|---|---|---|---|
| E92000001 | 1 | England | Y | 91.48 | 45 |
| U68943 | 553 | Chester South PCN | N | 88.57 | 95 |
List priority group indicators
Priority groups are collections of Indicators which are
displayed in the Regional & ICS
insights page of the CVD PREVENT website and can be accessed from
the API using the cvd_indicator_priority_groups()
function.
Here we return one indicator from each of the priority groups:
cvd_indicator_priority_groups() |>
dplyr::select(PriorityGroup, PathwayGroupName, PathwayGroupID, IndicatorID, IndicatorName) |>
dplyr::slice_head(by = PathwayGroupID) |>
gt::gt(row_group_as_column = T)| PriorityGroup | PathwayGroupName | PathwayGroupID | IndicatorID | IndicatorName |
|---|---|---|---|---|
| ABC | Hypertension | 5 | 4 | Percentage of patients aged 18 and over with GP recorded hypertension, who have had a blood pressure reading within the preceding 12 months |
| ABC | Cholesterol | 6 | 22 | Percentage of patients aged 18 and over with no GP recorded CVD and a GP recorded QRISK score of 10% or more, on lipid lowering therapy |
| ABC | Atrial Fibrilation | 7 | 7 | Percentage of patients aged 18 and over with GP recorded atrial fibrillation and a record of a CHA2DS2-VASc score of 2 or more, who are currently treated with anticoagulation drug therapy |
| CKD | Chronic Kidney Disease | 9 | 29 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5), with a record of an eGFR test in the preceding 12 months |
| Prevalence | NA | NA | 12 | Prevalence of GP recorded cardiovascular disease (wide definition) in patients aged 18 and over |
| Prevalence | Familial Hypercholesterolaemia | 8 | 9 | Prevalence of GP recorded possible, probable or confirmed familial hypercholesterolaemia, all ages |
| Smoking and BMI | Smoking | 10 | 24 | Percentage of patients aged 18 and over with GP recorded CVD or CVD risk factors who are GP recorded current smokers or have no smoking status recorded, whose notes record smoking status in the preceding 12 months. |
List pathway group indicators
Pathway groups are sub-groupings of Priority Groups visible in the Regional & ICS
insights page of the CVD PREVENT website and can be accessed from
the API using the cvd_indicator_pathway_group()
function.
Here we return the indicators within the ‘Chronic Kidney Disease’ pathway group (ID 9):
cvd_indicator_pathway_group(pathway_group_id = 9) |>
dplyr::select(PathwayGroupName, PathwayGroupID, IndicatorCode, IndicatorID, IndicatorName) |>
dplyr::group_by(PathwayGroupName) |>
gt::gt(row_group_as_column = T)| PathwayGroupID | IndicatorCode | IndicatorID | IndicatorName | |
|---|---|---|---|---|
| Chronic Kidney Disease | 9 | CVDP002CKD | 13 | Percentage of GP registered patients aged 18 and over, with 2 low eGFRs with no GP recorded CKD (G3a to G5) |
| 9 | CVDP001CKD | 8 | Prevalence of GP recorded CKD (G3a to G5) in patients aged 18 and over | |
| 9 | CVDP006CKD | 29 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5), with a record of an eGFR test in the preceding 12 months | |
| 9 | CVDP005CKD | 19 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5), hypertension and proteinuria, currently treated with renin-angiotensin system antagonists | |
| 9 | CVDP007CKD | 31 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5) with an ACR of less than 70 mg/mmol, in whom the last blood pressure reading (measured in the preceding 12 months) is less than 140/90 mmHg. | |
| 9 | CVDP010CHOL | 23 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5), who are currently treated with lipid lowering therapy. |
List Indicator Group indicators
Indicator Groups are further groups of indicators which are
classified by IndicatorGroupTypeID which indicates what
type of indicator, e.g. Priority Group.
Here we list the indicators under Indicator Group ID 13 (Monitoring) which lists ‘Key Question’ Indicator Group indicators:
cvd_indicator_group(indicator_group_id = 13) |>
dplyr::select(IndicatorGroupID, IndicatorGroupName, IndicatorGroupTypeName, IndicatorID, IndicatorName) |>
dplyr::group_by(IndicatorGroupID, IndicatorGroupName) |>
gt::gt()| IndicatorGroupTypeName | IndicatorID | IndicatorName |
|---|---|---|
| 13 - Monitoring | ||
| Key Question | 4 | Percentage of patients aged 18 and over with GP recorded hypertension, who have had a blood pressure reading within the preceding 12 months |
| Key Question | 29 | Percentage of patients aged 18 and over with GP recorded CKD (G3a to G5), with a record of an eGFR test in the preceding 12 months |
Return time series data for a metric and area
To get data over all available time points for a given metric use the
cvd_indicator_metric_timeseries() function whilst
specifying an area ID.
Here we visualise data for Salford South East PCN (area ID 705) for ‘AF: treatment with anticoagulants’ for women people aged 60-79 years (metric ID 130):
cvd_indicator_metric_timeseries(metric_id = 130, area_id = 705) |>
# prepare for plotting
dplyr::mutate(TimePeriodName = stats::reorder(TimePeriodName, TimePeriodID)) |>
ggplot2::ggplot(ggplot2::aes(
x = TimePeriodName,
y = Value,
group = AreaName ,
colour = AreaName)) +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::theme(
axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)
)
Or we can view the details of the time-series performance for this metric:
cvd_indicator_metric_timeseries(metric_id = 130, area_id = 705) |>
dplyr::select(AreaName, TimePeriodName, TimePeriodID, Value) |>
tidyr::pivot_wider(
names_from = AreaName,
values_from = Value
) |>
gt::gt()| TimePeriodName | TimePeriodID | England | Salford South East PCN |
|---|---|---|---|
| To March 2020 | 1 | 88.20 | 85.90 |
| To March 2021 | 2 | 88.60 | 86.00 |
| To September 2021 | 3 | 88.90 | 88.80 |
| To March 2022 | 4 | 89.33 | 90.00 |
| To June 2022 | 5 | 89.37 | 90.24 |
| To September 2022 | 6 | 89.64 | 90.60 |
| To December 2022 | 7 | 89.99 | 91.72 |
| To March 2023 | 8 | 90.97 | 90.12 |
| To June 2023 | 9 | 90.99 | 90.96 |
| To December 2023 | 15 | 91.15 | 92.22 |
| To March 2024 | 17 | 92.21 | 93.92 |
Return time series data for an indicator and area - inequalities
To see time series data for all metric breakdowns for an indicator
use the cvd_indicator_person_timeseries() function along
with the indicator ID and specify an area.
Here we view time-series metric data for indicator ‘AF: treatment with anticoagulants’ (ID 7) in Salford South East PCN (area ID 705), focussed just on the age group inequalities metrics:
cvd_indicator_person_timeseries(indicator_id = 7, area_id = 705) |>
dplyr::filter(
MetricCategoryTypeName == 'Age group',
!is.na(Value)
) |>
dplyr::mutate(TimePeriodName = stats::reorder(TimePeriodName, TimePeriodID)) |>
ggplot2::ggplot(
ggplot2::aes(
x = TimePeriodName,
y = Value,
group = MetricCategoryName,
colour = MetricCategoryName
)
) +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::theme(
axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)
)
Or we can view the details of the time-series performance for these metrics:
cvd_indicator_person_timeseries(indicator_id = 7, area_id = 705) |>
dplyr::filter(
MetricCategoryTypeName == 'Age group',
!is.na(Value)
) |>
dplyr::select(MetricCategoryName, TimePeriodName, TimePeriodID,
Value) |>
tidyr::pivot_wider(
names_from = MetricCategoryName,
values_from = Value
) |>
gt::gt()| TimePeriodName | TimePeriodID | 40-59 | 60-79 | 80+ |
|---|---|---|---|---|
| To March 2020 | 1 | 78.30 | 82.30 | 85.00 |
| To March 2021 | 2 | 91.30 | 82.30 | 84.80 |
| To September 2021 | 3 | 78.00 | 86.90 | 87.20 |
| To March 2022 | 4 | 76.60 | 86.81 | 90.23 |
| To June 2022 | 5 | 82.35 | 86.82 | 88.56 |
| To September 2022 | 6 | 80.00 | 86.56 | 90.60 |
| To December 2022 | 7 | 76.32 | 87.50 | 90.58 |
| To March 2023 | 8 | 72.73 | 87.42 | 91.06 |
| To June 2023 | 9 | 71.11 | 88.51 | 91.29 |
| To December 2023 | 15 | 74.42 | 88.94 | 90.61 |
| To March 2024 | 17 | 80.49 | 91.51 | 92.45 |
Return system level comparisons for a metric
Use the cvd_indicator_metric_systemlevel_comparison()
function to return metric performance for a specified time period for
all organisations in the same system level, for example for all GP
practices or for all PCNs.
Here we return performance for metric ‘AF: DOAC & VitK’ in people aged 40-59 years (metric ID 1270) in time period 17 for Salford South East PCN (area ID 705) and all other PCNs - truncated to a sample of four PCN performances:
cvd_indicator_metric_systemlevel_comparison(metric_id = 1270, time_period_id = 17, area_id = 705) |>
dplyr::filter(AreaID %in% c(705:709), !is.na(Value)) |>
dplyr::select(SystemLevelName, AreaID, AreaName, Value) |>
gt::gt()| SystemLevelName | AreaID | AreaName | Value |
|---|---|---|---|
| PCN | 705 | Salford South East PCN | 80.49 |
| PCN | 707 | Haringey - North East PCN | 88.00 |
| PCN | 708 | Teesdale PCN | 100.00 |
| PCN | 709 | Spen Health & Wellbeing PCN | 85.71 |
Metric area breakdown
Use cvd_indicator_metric_area_breakdown() function to
return the data Area Breakdown chart.
Here we return performance for metric ‘AF: DOAC & VitK’ in men aged 60-79 years (metric ID 128) in time period 17 for Salford South East PCN (area ID 705):
cvd_indicator_metric_area_breakdown(metric_id = 128, time_period_id = 17, area_id = 705) |>
dplyr::select(SystemLevelName, AreaID, AreaName, Value) |>
gt::gt()| SystemLevelName | AreaID | AreaName | Value |
|---|---|---|---|
| England | 1 | England | 92.49 |
| PCN | 705 | Salford South East PCN | 90.00 |
Misc
List external resources
To return a list of all external resources used by the API use
function cvd_external_resource(). Here we show the first
five external resources:
cvd_external_resource() |>
dplyr::filter(ExternalResourceID < 10) |>
dplyr::select(ExternalResourceCategory, ExternalResourceSource, ExternalResourceTitle) |>
dplyr::group_by(ExternalResourceCategory) |>
gt::gt(row_group_as_column = T)| ExternalResourceSource | ExternalResourceTitle | |
|---|---|---|
| Data Packs | Public Health England | Cardiovascular Disease Prevention Data Packs |
| NHS England | Equality and Health Inequality Packs | |
| Toolkits | NHS Digital | Weight Management Programme General Practice Toolkit |
| Primary Care Cardiovascular Society | CVD – identification and treatment of people at high risk | |
| Stratification tools | UCL Partners | Search and risk stratification tools |
Show data availability
To show data availability for a given system level in a given time
period then use the cvd_data_availability() function.
NB, data appears to be only available up to time period 6 (to September 2022).
cvd_data_availability(time_period_id = 6, system_level_id = 5) |>
dplyr::select(IndicatorShortName, IsAvailable, SystemLevelName, MetricCategoryTypeID) |>
dplyr::slice_head(n = 5) |>
gt::gt()| IndicatorShortName | IsAvailable | SystemLevelName | MetricCategoryTypeID |
|---|---|---|---|
| AF: prevalence | N | Practice | 4 |
| AF: prevalence | N | Practice | 2 |
| AF: prevalence | N | Practice | 1 |
| AF: prevalence | N | Practice | 3 |
| Hypertension: treatment to recommended age specific (79 and under) threshold | Y | Practice | 4 |