https://github.com/the-strategy-unit/dpp
Code and supporting files used as part of the impact evaluation of the Detect, Protect and Perfect (DPP) project to reduce stroke incidence by improving care of atrial fibrillation.
https://github.com/the-strategy-unit/dpp
atrial-fibrillation evaluation impact-evaluation nhs
Last synced: 10 months ago
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Code and supporting files used as part of the impact evaluation of the Detect, Protect and Perfect (DPP) project to reduce stroke incidence by improving care of atrial fibrillation.
- Host: GitHub
- URL: https://github.com/the-strategy-unit/dpp
- Owner: The-Strategy-Unit
- License: mit
- Created: 2025-05-12T16:37:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-13T10:29:39.000Z (about 1 year ago)
- Last Synced: 2025-05-13T11:24:51.017Z (about 1 year ago)
- Topics: atrial-fibrillation, evaluation, impact-evaluation, nhs
- Language: R
- Homepage:
- Size: 19.5 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
- Codeowners: CODEOWNERS
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# Detect, Protect and Perfect (DPP)
The [NHS Long Term Plan](https://www.longtermplan.nhs.uk/areas-of-work/cardiovascular-disease/) set out the aim of identifying and supporting patients at risk of stroke. Atrial fibrillation (AF) is a key risk factor for stroke and has become a focus for transformative interventions.
During 2022/23 and 2023/24, c.£26 million was invested in a funding programme to expand access to direct oral anticoagulants (DOACs) - [recommended by NICE](https://bnf.nice.org.uk/treatment-summaries/arrhythmias/#atrial-fibrillation) as the most effective treatment for preventing clots that can cause strokes in patients with AF. The programme had three pillars:
- **Detect:** reduce incidence of stroke by diagnosing more patients with AF
- **Protect:** ensure patients diagnosed with AF are offered anticoagulation, where clinically appropriate
- **Perfect:** ensure patients with AF are on the correct dose of the best value DOAC where clinically appropriate.
## About this repo
All data used in this analysis is within the public domain. Details regarding funding applications have been withheld.
The recommended approach to re-create the analysis is:
- Install dependencies,
- Obtain matching data,
- Obtain outcome data,
- Run the analysis files.
Details on how to perform each step are provided below.
### Installing dependencies
Run the [`get_dependencies.R`](scripts/get_dependencies.R) script to identify the packages used within this analysis. NB, this file requires at least `renv`, `janitor` and `dplyr` to first be installed on your system to run.
### Matching data
This analysis uses a Propensity Score Matching Differences-in-Difference (PSM-DiD) approach. In this method, GP practices which are part of the intervention group are matched with GP practices that are not part of any DPP project (the control group).
The variables used to match practices are grouped into clinical and socio-economic factors:
#### Clinical factors
| Matching variable | Rationale | Data source |
|------------------------|------------------------|------------------------|
| Proportion of the practice population aged 65 years or older | The risk of developing AF doubles with each progressive decade and exceeds 20% by age 80 years | [GP registered population data](https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice) by NHS Digital |
| Prevalence of obesity at the practice | Obesity (defined as BMI \>= 30) is an independent risk factor for the development of AF | [Fingertips](https://fingertips.phe.org.uk/) |
| Prevalence of diabetes at the practice | Diabetes mellitus is an independent risk factor for AF, especially in young people | [Fingertips](https://fingertips.phe.org.uk/) |
| Prevalence of hypertension at the practice | People with hypertension have a 1.7-fold higher risk of developing AF compared with people with blood pressure in the normal range | [Fingertips](https://fingertips.phe.org.uk/) |
| Gender proportion | Age-adjusted incidence, prevalence and lifetime risk of AF is higher in men compared with women | [GP registered population data](https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice) by NHS Digital |
#### Socio-economic factors
| Matching variable | Rationale | Data source |
|------------------------|------------------------|------------------------|
| Registered practice population per full time equivalent (FTE) clinician | The number of patients per clinical WTE can be a marker for a range of factors such as funding available to the practice, presence of other clinical staff, clinical needs of the local population and changes in the local population | [GP workforce data](https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services/30-april-2023) by NHS Digital |
| Weighted deprivation score per practice | Greater socio-economic deprivation is associated with poorer outcomes across a range of health issues, including AF | [Fingertips](https://fingertips.phe.org.uk/) |
| Rural-Urban classification of the practice | Distance can be a barrier to accessing healthcare. Rurality can be a proxy measure for ease of healthcare access and has implications in the care of people with AF | [Rural-Urban classification](https://www.ons.gov.uk/methodology/geography/geographicalproducts/ruralurbanclassifications) based on practice postcode |
These data for these variables need to be downloaded.
- Get a list of GP practice details from the [ODS](https://www.odsdatasearchandexport.nhs.uk/) API using [`get_gp_details.R`](scripts/get_gp_details.R)
- Get monthly details of GP practice registered populations from [NHS Digital](https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice) using [`get_gp_registered_patients.R`](scripts/get_gp_registered_patients.R)
- Process this data to extract out relevant details using [`get_gp_population_summary.R`](scripts/get_gp_population_summary.R)
- Get matching data from Fingertips using [`data_fingertips.R`](scripts/data_fingertips.R)
- Download [GP workforce data](https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services/30-april-2023) for April 2023
- Process GP workforce information using [`get_gp_workforce.R`](scripts/get_gp_workforce.R)
- Process all the matching data using [`get_matching_variables_df.R`](scripts/get_matching_variables_df.R)
### Outcome data
The outcome data was obtained from the [CVDPREVENT](https://www.cvdprevent.nhs.uk/) audit via their API.
Use the `get_cvd_data.R` script to download the data.
### Impute missing matching data
- Investigate the amount of missing data using [`dpp_matching_variables_missingness.qmd`](outputs/dpp_matching_variables_missingness.qmd)
- Perform multiple imputation to 'fill-in' missing matching data using [`dpp_impute_missing_matching_variables.qmd`](outputs/dpp_impute_missing_matching_variables.qmd)
### Assign GP practices to projects
GP practices are assigned to each funded intervention based on the geography specified as part of funding applications.
- Get a [lookup from GP practice to PCN, CCG, ICB and NHS Region](https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice/august-2024) from NHS Digital. Name this file `intervention_counterfac_lu.csv`.
- Get a list of funded initiatives and their respective start / end dates using [`get_dpp_project_final_list.R`](scripts/get_dpp_project_final_list.R) NB, this requires a copy of the funding application spreadsheet, which is not currently publicly available.
- Link funding applications with practices using [`link_grants_with_practices.R`](scripts/link_grants_with_practices.R) NB, this step is not possible without the above list of funding applications.
Some hospital-based projects have a unique list of practices or use a method of practice assignment which is based on dominant provider of care based on MSOA geography. To assign practices based on this approach you need to:
- Download a [lookup of hospital catchment areas](https://app.box.com/s/qh8gzpzeo1firv1ezfxx2e6c4tgtrudl) produced by Office for Health Improvement and Disparities (OHID). NB, this is the '2022 Trust Catchment Populations_Supplementary MSOA Analysis.xlsx' file.
- Download a lookup from postcode to MSOA from the [Open Geography Portal](https://open-geography-portalx-ons.hub.arcgis.com/datasets/ons::postcode-to-oa-2021-to-lsoa-to-msoa-to-lad-november-2024-best-fit-lookup-in-the-uk/about).
- Use [`get_gps_per_hospital.R`](scripts/get_gps_per_hospital.R) to produce catchment areas based on the dominant provider of care in each MSOA, which are then linked with GP practices based on postcode to MSOA lookup.
### Matching and impact analyses
The process of matching intervention GP practices with control GP practices and the impact analyses are done at national, case-study (aggregate) and case-study (individual) levels.
- The national-level analysis can be reproduced using [`dpp_programme_report_template.qmd`](outputs/dpp_programme_report_template.qmd).
- The case-study (aggregate) analyses can be reproduced using [`dpp_overall_report_template.qmd`](outputs/dpp_overall_report_template.qmd).
- The individual case-study reports can all be reproduced using [`dpp_gp_report_template.qmd`](outputs/dpp_gp_report_template.qmd). Which project the template produces a report for is controlled by the parameters set within the `yaml` header of the document:
- `project_id` controls which project is reported, using the 'P' nomenclature, where 'P1' is the ID for project 1.
- `alternate_data` is used where there are multiple ways of assigning GP practices to the intervention area. Setting this to `FALSE` will mean the default GP practice assignment method defined in `link_grants_with_practices.R` is used. Setting to `TRUE` (default) will use any alternative method of assignment, as detailed in the `dpp_gp_report_template.qmd` script.
- `alternate_data_details` is used to specify which of multiple GP practice assignment methods are to be used. Commonly used parameters include "emergency" and "af". "emergency" is used for hospital-based projects to assign GP practices based on MSOAs where the hospital is the dominant provider of emergency care. "af" is used for a single project in which only GP practices which undertook activities to improve care of atrial fibrillation are to be counted.
- `save_did` is used to control whether the underlying data is saved for future use with the `calc_percentage_difference.R` script.
``` yaml
---
params:
project_id: "P20"
alternate_data: TRUE
alternate_data_details: ""
save_did: TRUE
---
```