OBJECTIVES: Inequalities in health and their determinants are widening, and it is unclear whether these indicators are correlated and how policy should best address these inequalities. We therefore aimed to 1) identify and characterise geographical clusters in health and socio-economic inequalities in England; and, 2) assess the effect of reducing geographical inequalities in health.

METHODS: Data on 26 health and socio-economic indicators at small area level (149 Upper Tier Local Authorities) were extracted from English administrative databases, for the latest available year in 2015–2019. Pearson correlations between indicators at small area level were analysed. Areas with similar socio-economic profiles were grouped using hierarchical clustering, then health indicators were compared across clusters. Effects of interventions to improve health outcomes for all clusters to match the least deprived cluster were estimated.

RESULTS: Income and wealth inequalities correlated most strongly with health inequalities, while behavioural risk factors (smoking, obesity and alcohol consumption) correlated with life expectancy, healthy life expectancy and socio-economic indicators. Four key geographic clusters of socio-economic profiles were identified: (1) Northern cities and surrounding areas, Midlands cities, coastal cities; (2) rural areas; (3) inner-city London boroughs, Bristol and Brighton; and (4) home counties and outer London boroughs (from most to least deprived). The most deprived clusters displayed multi-dimensional health disadvantage: their residents had the lowest life expectancy and highest levels of depression, disability and child obesity. Improving health to match the least deprived cluster could potentially increase national life expectancy/healthy life expectancy at birth by 2.0/3.3 years, and reduce depression and childhood obesity prevalence by 3%, from 11% and 35% respectively. CONCLUSION: There are large inequalities in health outcomes that correlate and cluster with both income and wealth indicators. Granular estimates of variation in geographical patterns of inequalities and unmet need could inform more effective precision public health approaches.