Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Mamas Mamas
  • Harm Van Marwijk
  • Andrew M. Ryan
  • Bruce Guthrie
  • Tim Doran


Background: Primary care provides the foundation for most modern healthcare systems, and in the interests of equity should be resourced according to local need. We aimed to spatially describe the burden of chronic conditions and primary medical care funding in England at a low geographical level, and to measure how much variation in funding is explained by chronic condition prevalence and other patient and regional factors.
Methods: We used multiple administrative datasets including chronic condition prevalence and management data (2014-15), funding for primary care practices (2015-16), and geographical and area deprivation data (2015). Data were assigned to a low geographical level (average 1500 residents). We investigated the overall morbidity burden across 19 chronic conditions and its regional variation, spatial clustering and association with funding and area deprivation. A linear regression model was used to explain local variation in spending using patient demographics, morbidity, deprivation and regional characteristics.
Results: Levels of morbidity varied within and between regions, with several clusters of very high morbidity identified. At the regional level, morbidity was modestly associated with practice funding, with the North East and North West appearing underfunded. The regression model explained 39% of the variability in practice funding, but even after adjusting for covariates, large variability in funding existed across regions. High morbidity and, especially, rural location were very strongly associated with higher practice funding, while associations were more modest for high deprivation and older age.
Conclusions: Primary care funding in England does not adequately reflect the contemporary morbidity burden. More equitable resource allocation could be achieved by making better use of routinely available information and “big data” resources. Similar methods could be deployed in other countries where comparable data are collected, in order to identify morbidity clusters and to target funding to areas of greater need.

Bibliographical metadata

Original languageEnglish
Article number19
JournalBMC Medicine
Issue number1
Early online date14 Feb 2018
Publication statusPublished - 14 Feb 2018

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