Authors' objectives: Many countries use geographical funding formulae to distribute public funds for health care to local planning areas in proportion to need. In England, these aim to distribute resources in proportion to all healthcare needs regardless as to whether these are currently met or unmet. The National Health Service also has an additional objective to allocate resources to reduce health inequalities (i.e. differences in health between socioeconomic groups). Adjusting for unmet needs could help achieve this second objective, if a greater proportion of needs are unmet in disadvantaged socioeconomic groups with poorer health compared to more advantaged socioeconomic groups. Alternatively, if there are greater unmet needs for relatively expensive conditions that tend to affect older age groups (e.g. cancer), this could lead to a greater proportion of needs being unmet in more advantaged socioeconomic groups, who will tend to be older due to greater life expectancy. Adjusting for unmet needs would then lead to allocation of a greater share of resources to these more affluent populations with better health, potentially increasing health inequalities. It is, however, unclear how met and unmet healthcare needs should be measured in these formulae and how better accounting for unmet needs influences health inequalities. Many countries share out healthcare funding to local areas so that places with greater needs get more money than places with fewer needs. The National Health Service aims to account for unmet needs, for example undiagnosed conditions, in this process. The National Health Service also tries to share out resources to reduce the gap in health between more deprived and more affluent populations. It is unclear how providing a greater share of resources to those places with more undiagnosed conditions might influence this health gap. For 11 serious health conditions, we estimate the number of people diagnosed and undiagnosed and the costs of treating these conditions for each local health planning area in England. We estimate the share of the National Health Service budget that each area would receive in two scenarios. Firstly, providing each area with sufficient resources to treat the number of people diagnosed, and secondly, providing each area with sufficient resources to treat the number of people diagnosed or undiagnosed. We assess the likely effect on the gap in health between more deprived and more affluent populations of moving from the first to the second scenario. We estimate that more affluent areas would receive a greater share of National Health Service resources when the number of undiagnosed people were considered compared to when only diagnosed people were taken into account. This would widen the health gap between more deprived and more affluent populations. This is because, diseases that were more prevalent in older populations were estimated to be less likely to be diagnosed and more expensive to treat, and more affluent places tend to have older populations. Estimates of the number of people with undiagnosed conditions in each area are, however, very uncertain and better data are required to improve the accuracy. We provide a framework for improving these estimates.

Authors' results and conclusions: Based on the estimates of underdiagnosis used, areas with the lowest overall needs tended to have a greater proportion of their needs unmet. Adjusting resource allocation by accounting for these unmet needs due to underdiagnosis would move resources from areas with the highest level of needs to areas with lower overall needs. Moving to this ‘fair share distribution’ would tend to benefit less deprived areas more than more deprived areas, potentially widening health inequalities. We show how accounting for unmet needs due to underdiagnosis in allocating resources could widen health differences between more and less deprived areas when underdiagnosis and treatment costs increase with age. Further research is needed to confirm our provisional estimates, but we provide a useful framework for improving assessments of relative need for healthcare resource allocation. Alternative approaches are likely to be needed where resource allocation policy additionally aims to reduce health inequalities.

Authors' methods: We derive a synthetic data set for all people aged ≥ 30 years in England, in 2018, including age, sex, socioeconomic deprivation, region, local health planning area and whether people have diagnosed or undiagnosed long-term conditions. We calculated the annual primary and secondary care costs for each condition using linked electronic healthcare record data, then estimated needed expenditure for each health planning area for two scenarios: (1) when only accounting for diagnosed cases and (2) including all cases (diagnosed and undiagnosed). We examine how the distribution of need between places changes between these scenarios and the consequences of this for health inequalities.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hsdr/NIHR130258

Year Published: 2026

URL for published report: https://www.journalslibrary.nihr.ac.uk/hsdr/published-articles/GJBB0820

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/GJBB0820

Organisation Name: NIHR Health Services and Delivery Research programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk