Unlocking data to inform public health policy and practice: Exploring barriers and creating solutions for public health intelligence using integrated datasets across Kent, Sussex and Surrey (KSS)

The triangulation of population level health needs, data, and health service use is done by public health teams in local authorities (LAs). In the region of Kent, Surrey and Sussex (KSS), LAs, together with NHS partners, have launched programmes to create integrated datasets of citizens’ NHS, social care and administrative records. While the infrastructure for hosting and linking data is proceeding, to unlock the potential for these datasets to improve health and quality of life for the local population, several barriers must be tackled.

Currently, analytics capacity and capability in KSS public health teams is not at a level to deal with the complexity of the integrated data, and due to the pandemic, LAs have not had the capacity to develop those skills through training or recruitment. Second, legal and technical solutions for linkage of data to enable cross sector working (e.g. between health and housing or education) are needed.

Third, public involvement and engagement in various aspects of the use of routinely collected data to support decision making in local authorities and across health and care systems has been largely neglected.

In this project we will tackle each of these three barriers. We will create and foster a partnership network between academic researchers, data-intensive scientists, and public health practitioners with a focus on sharing expertise in the technical linkage and analysis of multi-dimensional integrated datasets, kickstarted by two data-sprint events. Through this network, and by funding two 6-month analyst fellowships, we will support knowledge exchange, training and capacity in the public health teams, and tackle important public health case studies identified by our local authority partners (‘learning by doing’).

In Sussex, the fellow will explore how to use integrated data to identify patterns of service use or clusters of factors which are robust proxies for being at high risk of decline in health status, multimorbidity, or increase in care needs. This would inform better targeted early intervention, and improvement in provision of more holistic and less duplicated care across services.

In Kent, the fellow will explore technical solutions within current legal frameworks for linking non-NHS, non-social care data, such as from housing, education and employment, with health and care datasets, to explore of socio-economic and wider determinants of health. These approaches will maximise the use of different sources of data to facilitate cross-sector working across service providers, to gain a greater understanding of health needs and health inequalities, particularly focussing on the local needs of promoting early & preventive service use.

In addition, they will reduce inefficient provision or duplication for people using multiple services, both of which are key public health and LA focus.

Finally, we will conduct a series of deliberative focus groups to understand public perception of the use of routinely collected data to support decision making in local authorities and ensure public involvement is embedded in dataset governance structures.

We will disseminate results from projects through bespoke stakeholder and public-facing events, reports, journal articles, and regional and national conferences. This work will enable a step-change in productivity in terms of analysing citizens’ data for local and regional public health intelligence to improve health for the local population.