NEF Consulting were commissioned by NHS England to undertake the second phase of a project begun in 2017 aimed at better understanding the economic case for support to carers. The work involved developing a cost-benefit model which can be populated with local data by ICSs/STPs (collaborations formed by NHS organisations and local councils) to enable costed, evidence-based business cases for intelligent, locally targeted carer support.
The model places the total cost at between £24bn and £37bn each year, and growing.
With growing national dependence on the unpaid carer population and extreme budgetary pressures on health and social care commissioning bodies, there is increasing interest in better understanding, and quantifying, the relative merits of providing support services directly aimed at carers.
To our knowledge, the full range of costs and benefits associated with unpaid care has not previously been aggregated nor systematically estimated at a population level.
The scoping phase for developing the model involved:
- A review of the initial first qualitative study conducted in 2017, wider literature, research, and data.
- Scoping interviews with members of three ICSs/STPs and Carers UK.
From this scoping work we distilled a wide range of potential model data sources and parameters. The most notable data sources, underpinning the model’s primary functions, are the 2017 and 2018 GP Survey, and the HSE Survey.
During scoping we also established a categorisation of the types of intervention targeted at carers:
- Working with employers to help them understand and support carers in their organisations.
- Carer support groups.
- Respite services.
- Carer training programmes.
We refined and simplified the outcomes framework established in the scoping study to make it suitable for conversion into a credible economic model.
Outcomes were distilled from the logic models designed in Phase One of the project, the scoping interviews, and the literature reviews as follows:
|Carer||Mental health-related welleing|
|Social relationships-related wellbeing|
|Employment/job – related wellbeing (double counting with two outcomes above was accounted for)|
|State (NHS)||Mental health care costs (carer)|
|Physical health care costs (carer)|
|Physical health care costs (cared-for)|
|State (local authorities)||Costs of (avoidable) residential care (cared-for)|
|State (undefined)||Cost of professional care worker|
|Cared-for person||Mental health-related wellbeing|
|Physical health-related wellbeing|
|Costs of (avoidable) residential care|
The modelling work addressed two questions about the carer population:
- What are the direct and indirect costs of providing unpaid care (both to carers themselves and to different stakeholders) across the outcomes?
- What scale of value could be achieved across stakeholders if carers were better supported to improve across these outcomes?
The model approaches these questions through the ‘baseline approach’ and ‘interventions modelling’:
- The baseline approach offers an understanding of the scale of costs associated with different stakeholders in the provision of unpaid care, by comparing carers’ outcomes, and their implications for public services and benefits, with the non-carer population.
- The interventions modelling offers an understanding of how different sources of support can affect different stakeholder outcomes.
The model developed for NHS England is designed to support and inform service commissioning relating to unpaid carers in England. It represents our best attempt to aggregate across disparate data sources to build a social cost-benefit model that captures a wider range of wellbeing outcomes than previously achieved. This model is designed to stimulate thinking, it is there to be challenged, to evolve or to be superseded.
NEF Consulting’s key ‘take-home’ messages from developing this model were as follows:
- Unpaid carers make a significant economic contribution to society – their labour is currently worth between £54 and £86 billion per year in England alone.
- An additional benefit arises because unpaid care cannot always be substituted with paid care. In some cases, when the state takes over, the person being cared for has to move into residential care, at greater cost to both the patient, and the state. Our attempt at valuing this benefit places it between £1.2 and £5.9 billion per year, split between the patient and the tax payer.
- Unpaid carers are not ‘free’, there are a wide variety of social and economic costs to society associated with their labour. We place these costs at between £24 and £37 billion per year.
- These costs range through lost earnings from employment (to the carer and to the state), to our monetised estimates of the pain/discomfort, anxiety/depression, and social isolation they experience, and the costs these ultimately represent to society and particularly the NHS.
- Using the Quality Adjusted Life Year (QALY) approach to budgeting healthcare spending we crudely estimate that it would be appropriate for the state to spend £4.4 to £5.9 billion every year attempting to mitigate, and treat the pain/discomfort and anxiety/depression outcomes experienced by unpaid carers. This would translate into a spend of around £606 to £795 per carer per year.
- Data is not available for the actual spend supporting carers, but it is believed to be significantly less than our lower estimate.
- Modelling a crude typology of interventions aimed at unpaid carers (working with employers to ease burden, facilitating support groups and networks, providing skills training in care, and providing respite services) we show that astute spending could generate strong social benefit-cost ratios of 4:1 and above.
- Across the interventions modelled we found good evidence for high returns to investment in support groups and skills training, and moderate returns to working with employers.
- We faced problems valuing respite care. The evidence base around respite care remains controversial, and unreliable. We struggled to find studies which quantitatively measured the benefits widely reported in qualitative research.