Public Health Outcomes Framework

             

I initially saw the notice of the August update via twitter @PHE_uk, but I later found the full future publication timeline available at gov.uk.

All their new statistics are summarised in their blog Public Health Matters, and the data is available online with interactive plots through their Fingertips service.

It summarises 33 different National Public Health Profiles, including the most recent version of the Public Health Outcomes Framework.

While I was prepared to download information I wanted directly through the site - I found their fantastic package tool ‘fingertipsR’ which allows me to import PH data from all profiles directly into the R environment through their API.

#install.packages("devtools")
#devtools::install_github("PublicHealthEngland/fingertipsR")
library(fingertipsR)
library(tidyverse)

The package is explained here.

help("fingertipsR")

I chose to download the most recent PH Outcomes Framework, and then inspect the data for Kent (filtering in England and the Subnational Parent).

PHOF <- fingertipsR::fingertips_data(ProfileID = 19)

Kent <- PHOF %>%
  filter(AreaName=='Kent'|AreaName=='England'|AreaName=='South East region')

Inspecting it, I can see there are 169 seperate Indicator Names.

unique(Kent$IndicatorName)

I’ve chosen to demonstrate the data for ‘0.1i - Healthy life expectancy at birth’.

Kent %>%
  filter(IndicatorID=='90362') %>%
  ggplot(aes(x=Timeperiod)) +
  geom_col(aes(y=Value, fill=Sex), position = "dodge") +
  geom_errorbar(aes(ymin = LowerCIlimit, ymax = UpperCIlimit, fill=Sex), width = 0.5, position = position_dodge(width = 0.9)) +
  geom_text(aes(label = signif(Value,3), y=Value, fill=Sex), vjust=5, position = position_dodge(width = 0.9), colour="white") +
  labs(y = "Healthy life expectancy at birth") +
  theme(legend.title=element_blank()) +
  theme(axis.title.x=element_blank()) +
  facet_grid(AreaName ~ .)

This was an interesting way to explore Public Health England Data through R. The next steps for me will be to explore new HTML widgets as a way to convert R data into interactive plots.

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