BACKGROUND: 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. AIM: We outline a framework for estimating the relative need in geographical healthcare resource allocation and show how the distribution of needed resources between local health planning areas in England changes when accounting for unmet needs due to underdiagnosis for 11 long-term conditions. DESIGN: 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. RESULTS: 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. CONCLUSION: 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. FUNDING: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR130258.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)