Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis
Abstract Background Primary care provides the foundation for most modern health-care systems, and in the interests of equity, it should be resourced according to local need. We aimed to describe spatially the burden of chronic conditions and primary medical care funding in England at a low geographi...
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doaj-54f8ea06b1fd498d9cc980b7f09e7ec82020-11-25T00:59:18ZengBMCBMC Medicine1741-70152018-02-0116111310.1186/s12916-017-0996-0Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysisEvangelos Kontopantelis0Mamas A. Mamas1Harm van Marwijk2Andrew M. Ryan3Peter Bower4Bruce Guthrie5Tim Doran6Division of Population Health, Health Services Research & Primary Care; Faculty of Biology, Medicine and Health, University of ManchesterScience and Technology in Medicine, Keele UniversityDivision of Population Health, Health Services Research & Primary Care; Faculty of Biology, Medicine and Health, University of ManchesterSchool of Public Health, University of MichiganDivision of Population Health, Health Services Research & Primary Care; Faculty of Biology, Medicine and Health, University of ManchesterPopulation Health Sciences Division, School of Medicine, University of DundeeDepartment of Health Sciences, University of YorkAbstract Background Primary care provides the foundation for most modern health-care systems, and in the interests of equity, it should be resourced according to local need. We aimed to describe spatially 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 data sets 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, a large amount of 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, to identify morbidity clusters and to target funding to areas of greater need.http://link.springer.com/article/10.1186/s12916-017-0996-0Primary care fundingChronic conditionsMorbidityDeprivationSpatial clusteringQuality and Outcomes Framework |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Evangelos Kontopantelis Mamas A. Mamas Harm van Marwijk Andrew M. Ryan Peter Bower Bruce Guthrie Tim Doran |
spellingShingle |
Evangelos Kontopantelis Mamas A. Mamas Harm van Marwijk Andrew M. Ryan Peter Bower Bruce Guthrie Tim Doran Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis BMC Medicine Primary care funding Chronic conditions Morbidity Deprivation Spatial clustering Quality and Outcomes Framework |
author_facet |
Evangelos Kontopantelis Mamas A. Mamas Harm van Marwijk Andrew M. Ryan Peter Bower Bruce Guthrie Tim Doran |
author_sort |
Evangelos Kontopantelis |
title |
Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis |
title_short |
Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis |
title_full |
Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis |
title_fullStr |
Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis |
title_full_unstemmed |
Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis |
title_sort |
chronic morbidity, deprivation and primary medical care spending in england in 2015-16: a cross-sectional spatial analysis |
publisher |
BMC |
series |
BMC Medicine |
issn |
1741-7015 |
publishDate |
2018-02-01 |
description |
Abstract Background Primary care provides the foundation for most modern health-care systems, and in the interests of equity, it should be resourced according to local need. We aimed to describe spatially 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 data sets 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, a large amount of 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, to identify morbidity clusters and to target funding to areas of greater need. |
topic |
Primary care funding Chronic conditions Morbidity Deprivation Spatial clustering Quality and Outcomes Framework |
url |
http://link.springer.com/article/10.1186/s12916-017-0996-0 |
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