Unmet Need and the Public Health Grant: A Data-Driven Tool for Local Authority Spending Decisions
Author(s)
George R. Daly, PhD.
Population Health, SCHARR, University of Sheffield, Sheffield, United Kingdom.
Population Health, SCHARR, University of Sheffield, Sheffield, United Kingdom.
OBJECTIVES: The Public Health grant funds local services in England aimed at improving health outcomes, easing healthcare system pressures, and addressing social inequalities. In 2025/26, the grant increased by £209 million (3% in real terms) to £3.869 billion, though this remains well below historical levels. Compounded by inflationary pressures and demographic shifts, public health services face outdated procedures, historic allocation disparities, and acute backlogs. These factors have led to increasing Unmet Need, where services fall short of statutory requirements, current demand, or peak provision. This research aims to identify, characterise, and measure Unmet Need across local authorities in England.
METHODS: A mixed methods approach was used to assess the prevalence and distribution of Unmet Need. Open-source data from 152 upper-tier local authorities over a 9-year period were collated and analysed. Spending, service delivery, and population indicator data were used to estimate provision gaps across service areas, demographics, geography, and time. Qualitative insights were gathered through surveys and interviews with directors of public health (DPHs) to identify key themes.
RESULTS: High post-pandemic inflation has driven recent funding shortfalls and obscured a pre-existing link between rising need and deprivation. On average, public health services now receive two-thirds of their previous peak funding, with non-mandated services facing deeper cuts as limited budgets are redirected to mandated provision. A digital tool is in development to help local authorities identify areas of Unmet Need and better target resources and advocate for funding.
CONCLUSIONS: Despite modest funding increases, Unmet Need continues to grow as services remain under-resourced relative to historical benchmarks. Addressing this will require re-evaluating funding formulas, modernising delivery systems, and improving data tools. Future work will integrate prevalence and staffing data to refine the tool and support evidence-based decision-making for local authorities and DPHs.
METHODS: A mixed methods approach was used to assess the prevalence and distribution of Unmet Need. Open-source data from 152 upper-tier local authorities over a 9-year period were collated and analysed. Spending, service delivery, and population indicator data were used to estimate provision gaps across service areas, demographics, geography, and time. Qualitative insights were gathered through surveys and interviews with directors of public health (DPHs) to identify key themes.
RESULTS: High post-pandemic inflation has driven recent funding shortfalls and obscured a pre-existing link between rising need and deprivation. On average, public health services now receive two-thirds of their previous peak funding, with non-mandated services facing deeper cuts as limited budgets are redirected to mandated provision. A digital tool is in development to help local authorities identify areas of Unmet Need and better target resources and advocate for funding.
CONCLUSIONS: Despite modest funding increases, Unmet Need continues to grow as services remain under-resourced relative to historical benchmarks. Addressing this will require re-evaluating funding formulas, modernising delivery systems, and improving data tools. Future work will integrate prevalence and staffing data to refine the tool and support evidence-based decision-making for local authorities and DPHs.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
EPH270
Topic
Epidemiology & Public Health, Methodological & Statistical Research, Real World Data & Information Systems
Topic Subcategory
Public Health
Disease
No Additional Disease & Conditions/Specialized Treatment Areas