December 2023
Navigation to other PIP analysis webpages:
Country: UK
Source for PIP data (from DWP): Personal Independence Payment statistics.
Stat-Xplore system for DWP data: Stat-Xplore databases
Source for Vaccination data: NHS - COVID-19 vaccinations archive.
Source for Population Estimates (UN - Population division): UN table: Population estimates.
The data available from the DWP PIP program includes disability claim caseload, new registrations, and cases that had been cleared (aka clearances) in that month. “New registrations” data does not provide information on disability type (i.e. disease or condition), but clearance data and total caseload data does provide this information. The only other claimant characteristics provided are sex and 5-year age group.
It should be noted that PIP replaced the UK’s previous Disability Living Allowance (DLA) system in 2013. As such, you may see a sharp increase in cases/claims in the few years following the initiation of PIP, which has been explained as “capacity issues” by the DWP; for this reason, only cases after January 2016 are included in this set of analyses.
A person may be eligible to claim PIP if they are between the ages of 16 to 64 and have a health condition or disability that:
In this analysis we investigate trends in monthly PIP clearances of new claims for different causes. We compare the 2016 to 2019 trendline in PIP clearances with actual claims and compute the deviation from trend in absolute terms, relative terms (percentage deviation), and the deviation from trend Z-score (number of standard deviations from trend). To visualise the different computations, the user can perform the respective selection in the interactive chart below.
The user can select the desired age group and cause for the disability claim. The disability causes can be displayed by choosing the respective body systems that underlie the respective cause of disability. The user can also select the cause by showing the top 20 common causes (in absolute number).
The plots also show the cumulative vaccine rollout (all doses) for the selected age group, as a percentage of the population, as we observe that the increase in many disability claim causes are correlated to the vaccine rollout, in particular when observing younger age groups.
The monthly data provides a granularity that creates the possibility of investigating disability claims in relation to contemporaneous pandemic-related events such as vaccinations, lockdowns, and COVID mortality.
In this analysis we investigate trends in yearly PIP clearances of new claims for different causes. We compare the 2016 to 2019 average in PIP clearances with actual claims and compute the deviation from average in absolute terms, relative terms (percentage deviation), and the deviation Z-score (number of standard deviations). To visualise the different computations, the user can perform the respective selection in the interactive chart below. Please note that Z-scores computed in the yearly analysis are based upon the standard deviation of excess clearances for 2016 to 2019, which comprise of only 4 datapoints. Consequently, these values could easily exaggerate the statistical significance of the underlying signal. For a more reliable measure of statistical significance, the Z-scores provided using the monthly analysis are preferred.
The user can select the desired age group and cause for the disability claim. The disability causes can be displayed by choosing the respective body systems that underlie the respective cause of disability. The user can also select the cause by showing the top 20 common causes (in absolute number).
The plots also show the cumulative vaccine rollout (all doses) for the selected age group as a percentage of the population at 31st of December of the respective year.
The yearly data does not provide a granularity that creates the possibility of investigating disability claims in relation to contemporaneous pandemic-related events such as vaccinations, lockdowns, and COVID mortality. It does, however, provide interesting information in the sense that the annual averages smoothen seasonality trends or temporary changes in the PIP system.
The yearly data for 2023 refers to annualised values, with the last datapoint being October 2023.