Health checks aim to improve the health of the population by identifying individuals with risk factors earlier and intervening to prevent disease. The role of commissioners is to ensure health checks provide as much benefit as possible for taxpayer funds invested into them. As such, evidence of the potential impacts of different commissioning choices is beneficial in this decision-making process. System dynamics modelling can be used to provide this evidence by modelling the health check programme using a pre-existing cohort model of a given population. This modelling considers local data, literature findings, and stakeholder views, from which nine different scenarios of a local health check programme have been tested. These scenarios found that extending the duration of health checks to 20 years and improving treatment uptake for those with high blood pressure or high cholesterol reduced rates of cardiovascular disease, improved healthy life expectancy and reduced years lived in ill health. In contrast, improving attendance in the most deprived quintile of the population made very little change to the health of the population overall, although a larger effect was observed in the most deprived areas. These findings helped guide local commissioning decisions by showing the long-term impact of different health check scenarios.

England’s population is living longer, a sign of progress and better health, but adult social care (ASC) services must adapt to support a growing number of older residents, who may need help to remain independent, safe, and well. Kent County Council (KCC), in South East England, projects a 28% and 53% increase in its residents aged 65+ and 85+, respectively, over the next decade. This study aimed to inform the development of KCC’s ASC Prevention Framework using a System Dynamics Modelling (SDM) approach to evaluate the impact of preventive interventions on ASC demand and expenditure. Using linked local health and social care data and the Johns Hopkins ACG® tool, the 1.3 million adult population was stratified into Patient Needs Groups. Analyses showed that higher ASC costs were associated with being older females, living alone, deprivation, and frailty-related indicators such as dementia, history of falls, etc. Around 28% of older adults aged 65+ accounted for 80% of ASC costs within that cohort, and related scenario testing projected a 48% rise in ASC costs over 10 years without interventions, moderated to 33% with targeted prevention. These findings demonstrate the value of integrated data and modelling to inform strategic, prevention-focused ASC planning.