Key Learnings from CPP & The King's Fund Workshop on Poverty Metrics

This workshop, held on 6th July 2022, explored what better data on poverty might look like in the context of population health and inclusive growth.

17 August 2022

6 minute read


Poverty and poor health are closely interrelated and act as barriers to inclusive growth. Tackling poverty should therefore be a priority for newly established Integrated Care Boards and their local partners. As outlined in Rosie Fogden’s CPP blog, strategic and operational data have been identified as key enablers of change in local health and care systems and with this in mind, the workshop brought together practitioners from NHS and local/regional government settings to co-design useful operational data and metrics for places focusing on poverty.

There is a challenge in identifying the best part of local agencies to coordinate collaborations in this space, particularly when public health teams and the NHS are consumed by existing agendas. Data could help to overcome that reticence as service leaders seek to develop a population health agenda.

Questions for discussion included:

  1. How can shared outcomes and metrics help to institutionalise better collaboration between local public services on poverty?
  2. What metrics of improvement would be useful to leaders at varied levels of the system?
  3. How would you use new datasets to better target interventions and joined-up effort?
  4. What data would drive action rather than description or commentary?

Poverty Truth Commissions

Alongside data, peoples’ lived experiences should also inform policy and service delivery. North of Tyne is the first Mayoral Combined Authority (MCA) to run a Poverty Truth Commission, which works to understand the features of poverty within an area and come up with practical solutions for policy change. They are one example of how public organisations can co-produce and co-design strategies and can help to identify which metrics to track and to test service design.

Existing poverty metrics

Measuring poverty is not an exact science, and as a result limited data is available at local level. What is available is not updated frequently. The best publicly available information at local level are the annual statistics on children in low-income families, which combine a number of sources including national survey and benefit claimant data. Universal credit data is updated more frequently but isn’t a measure of poverty per se and does not capture those outside the system who are not claiming benefits. Local authority level maps comparing the latest data on child poverty and universal credit claimants with the English Indices of Deprivation are available on CPP’s website. There are some examples of where researchers have used consumer spending and credit data to build more timely local profiles but this data is often not publicly available.

What metrics already exist at local level?

Using data to identify at risk patients: an example from the Children’s Hospital Alliance

Missing appointments is one of the top 10 causes of avoidable child death and children in the most deprived decile are more than twice as likely not to attend appointments due to the cost of transport, and the affordability of their parents taking time off work to attend. To tackle this, the Children’s Hospital Alliance designed a tool to identify those at risk of non-attendance so that they can be contacted and supported to attend. This is a really great example of hospitals working together and getting data sharing agreements in place to use individual level data to refer cases for further support.

Innovation to tackle inequalities:

Reflections from participants:

Participants shared lots of examples of systems trying to join the dots on the data, but none claimed to have solved the problem or identified a single data point to identify poverty. This is especially the case post-pandemic, and in the midst of a cost-of-living crisis, as the scale and nature of poverty in places is changing. Accessing individual or household data felt important for mapping out the new shape of poverty and for taking mitigating actions in some settings, yet this was often the gap, particularly at combined authority and integrated care board level. Some of the most developed work outlined was in children and young peoples’ services. This may reflect a different relationship between the state and households, the universal parent, or may reflect the more holistic and anticipatory nature of health services in this field.

Attendees reiterated the importance of nuance and qualitative narrative and were keen that this is not overwritten or ignored amid the data. To this end, how data and evidence are communicated was considered really important. There was also discussion about how funding models for health systems could be re-purposed to focus on poverty or deprivation, taking example from the pupil premium model in schools, which allocates school funding based on the number of deprived pupils. This would demonstrate the importance of this agenda and could ultimately motivate change.

Examples of relevant work:

The West Midlands Regional Economic Development Institute (WMREDI) Data Lab

This Institute is part of a partnership between the University of Birmingham and the West Midlands Combined Authority. The data lab brings together a number of policy-relevant tools and dashboards including predictive tools to spot where children may need support and where homelessness is likely to rise across the region.

Digital Exclusion Risk Index tool developed by Greater Manchester MCA

In October 2021 Greater Manchester published a national tool which identifies the risk of digital exclusion at local authority level based on demographics, deprivation and access to broadband. The tool intends to help local councils to prioritise digital inequity funding and initiatives.

The Centre for Progressive Policy are working with The King’s Fund on a series of case studies which will provide more detail on initiatives such as these, where places are using data to tackle the various facets of poverty.