How to use the annual PROMs data

Note: This guide is for illustration purposes only and may contain errors. It demonstrates example usage of the dataset and functions, but results should not be interpreted as validated analyses.

This example demonstrates how to explore annual NHS Talking Therapies PROMS (Patient Reported Outcome Measures) data using the nhstt package. We’ll look at effect sizes for depression treatment across selected therapy types to illustrate the basic workflow for accessing and visualising this publicly available data.

Setup

We start by loading the necessary packages and downloading the annual PROMS dataset with get_proms_annual(). This report contains clinical outcome measures including PHQ-9, GAD-7, and WSAS scores for NHS Talking Therapies services across England.

# Load packages
library(nhstt)
library(ggplot2)
library(dplyr)
library(tidyr)
library(stringr)

# Get PROMS data
# When running this for the first time it will download and tidy the reports
proms <- get_proms_annual()

Select data for analysis

Here we filter the data to focus on effect sizes for GAD-7 and PHQ-9 measures at the national England level for depression, selecting six specific therapy types: applied relaxation, CBT, collaborative care, brief psychodynamic psychotherapy, and computer-based guided and non-guided self-help. This step requires careful exploration of the underlying data structure.

selected_proms <- proms |>
  filter(org_type == "England") |>
  filter(!org_name == "UNKNOWN") |>
  filter(variable_a == "Depression") |>
  filter(
    variable_b %in%
      c(
        "Applied relaxation",
        "Cognitive Behaviour Therapy (CBT)",
        "Collaborative Care",
        "Brief psychodynamic psychotherapy",
        "Guided Self Help (Computer)",
        "Non-Guided Self Help (Computer)"
      )
  ) |>
  filter(measure_name %in% c("phq", "gad")) |>
  filter(measure_statistic %in% c("effect_size")) |>
  mutate(
    measure_name = factor(
      measure_name,
      levels = c("gad", "phq"),
      labels = c("GAD-7", "PHQ-9")
    ),
    measure_statistic = factor(
      measure_statistic,
      levels = c("effect_size"),
      labels = c("Effect size")
    )
  )

Create figure

Finally, we visualise the effect sizes for GAD-7 and PHQ-9 across the selected therapy types for depression treatment in England, showing trends for two reporting periods.

GAD-7 and PHQ-9 effect sizes by therapy type for depression treatment in England.