Social inequalities and primary care networks: realising the potential through advancing data and analytics

There are striking socioeconomic inequalities in illness and mortality in England. In the least-deprived fifth of areas, people can expect to have two long-term conditions by the time they are 71 years old. In the most-deprived fifth, this same level of illness is reached a decade earlier. Much of this inequality is preventable and reducing these inequalities requires cross-government action. But how can data and analytics help reduce health inequalities? 

The NHS Long Term Plan places increasing emphasis on partnerships between the NHS and care providers to tackle the social determinants of health. Primary care networks (PCNs) have the potential to be part of the solution to reduce inequalities and working towards this is one of their seven service specifications. 

As the complexity of care pathways across a wider range of providers increases, advances in data and analytics could help these partnerships to drive forward progress on inequalities and outcomes for patients. 

Linking data to identify socioeconomic inequalities in care pathways 

The emerging networks of general practices and other community services, including social care and voluntary sector organisations, will generate huge amounts of data. Unfortunately, each care provider often holds their data in a silo, meaning a patient's journey can't be followed through the system. 

It is encouraging to see that data sharing agreements and interoperability of information systems to support individual patient care across general practices are a pre-requisite as general practices form networks. Beyond this, it is vital we support data linkage of records from across general practices and other community providers to understand the total package of care provided by these wider partnerships for each patient over time. Analysts could then use the linked data to monitor population care needs, care quality and outcomes across the full range of community-based services that can impact health. 

These datasets would be essential to monitor differences across social groups defined by ethnicity, gender, age, and area-level deprivation for key indicators of care and to support delivery on the inequalities service standards. They could be used to undertake large-scale evaluation of the long-term impact of primary care initiatives to reduce inequalities – the impact of social prescribing on social and health outcomes, for example. 

At present, most health records don’t capture individual level indicators of socioeconomic deprivation that affect health – a person’s housing situation or economic poverty, for example. We therefore rely on area-level deprivation data to monitor socioeconomic inequalities in health care. However this is an imprecise measure as many socioeconomically deprived individuals don’t live in socioeconomically deprived areas. Without individual socioeconomic data, we won’t know if PCN initiatives reach those with the greatest social needs and not just the more advantaged patients in the more deprived areas. With linkage to person-level data such as housing tenure or receipt of benefits, we can access a more precise measurement of a patient’s social and economic circumstances than is currently available. In this way, linkage to local authority, social care and voluntary sector records enriches our understanding of the patient’s social context and will enable us to better direct health inequalities funding to target care for deprived individuals. 

Supporting data linkage and analysis 

Calls for data linkage to better cater to the health needs of socially excluded people have been made before. But as PCNs are being formed and other partnerships within and beyond the NHS are developed to further facilitate integrated care, now is the time to recognise what a valuable asset data can be. PCNs, within their wider integrated care system or sustainability and transformation partnership, will play a key part in population health management and will need to collect and analyse data to support this. Locally there are examples of successful data linkage that have enabled quicker care decisions and smoother treatment pathways for individual patients. These include: 

• Leeds Care Record 

• Whole Systems Integrated Care (WSIC) Dashboards and Information Sharing at North West London CCGs 

• Whole System Integrated Care Mental Health Dashboard by Imperial College Health Partners 

• NHS England investing in Local Health and Care Record Exemplars 

Arguably, their full potential for improved intelligence to plan services for whole populations has yet to be realised. 

We are learning from these exemplars about the foundations needed to support data linkage. This includes an information governance framework that allows different information systems to talk to each other to facilitate secure and timely data linkage – one that is endorsed by patients, providers and the public, and securely protects their privacy. This should include data from providers beyond the NHS as the PCNs develop their partnerships with other community service providers. Given the legal and technical challenges in setting up, this can’t be left to individual PCNs to develop. Higher-level support will be required. 

To turn new data linkages into insightful analysis that can help reduce social inequalities in health, we also need to invest in developing analytical capability. Our colleagues have highlighted the potential that can be unlocked with more investment in health and care data analytics within the NHS. If we extend that investment to support and develop analysts working across the multiple partners that form PCNs, we can learn more about social factors interacting with the health care system. 

PCNs will vary in their readiness to set up and use data linkage and this may further exacerbate inequalities. But with support to spread innovation in linking and analysing patient records across organisations, we may realise the benefits of data linkage for the most disadvantaged patients and communities. 

Dr Mai Stafford (@stafford_xm) and Kathryn Dreyer (@kathrynadreyer) are Principal Data Analysts, and Dr Sarah Deeny (@SarahDeeny) is an Assistant Director, Data analytics at the Health Foundation. Dr Rebecca Fisher (@BecksFisher) is a GP in Oxford and a Senior Policy Fellow at the Health Foundation.