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How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study
BACKGROUND: Reducing unplanned hospital admissions is a key priority within the UK and other healthcare systems, however it remains uncertain how this can be achieved. This paper explores the relationship between unplanned ambulatory care sensitive condition (ACSC) admission rates and population, ge...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445441/ https://www.ncbi.nlm.nih.gov/pubmed/28545412 http://dx.doi.org/10.1186/s12875-017-0638-9 |
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author | Busby, John Purdy, Sarah Hollingworth, William |
author_facet | Busby, John Purdy, Sarah Hollingworth, William |
author_sort | Busby, John |
collection | PubMed |
description | BACKGROUND: Reducing unplanned hospital admissions is a key priority within the UK and other healthcare systems, however it remains uncertain how this can be achieved. This paper explores the relationship between unplanned ambulatory care sensitive condition (ACSC) admission rates and population, general practice and hospital characteristics. Additionally, we investigated if these factors had a differential impact across 28 conditions. METHODS: We used the English Hospital Episode Statistics to calculate the number of unplanned ACSC hospital admissions for 28 conditions at 8,029 general practices during 2011/12. We used multilevel negative binomial regression to estimate the influence of population (deprivation), general practice (size, access, continuity, quality, A&E proximity) and hospital (bed availability, % day cases) characteristics on unplanned admission rates after adjusting for age, sex and chronic disease prevalence. RESULTS: Practices in deprived areas (at the 90th centile) had 16% (95% confidence interval: 14 to 18) higher admission rates than those in affluent areas (10th centile). Practices with poorer care continuity (9%; 8 to 11), located closest to A&E (8%; 6 to 9), situated in areas with high inpatient bed availability (14%; 10 to 18) or in areas with a larger proportion of day case admissions (17%; 12 to 21) had more admissions. There were smaller associations for primary care access, clinical quality, and practice size. The strength of associations varied by ACSC. For example, deprivation was most strongly associated with alcohol related diseases and COPD admission rates, while continuity of primary care was most strongly associated with admission rates for chronic diseases such as hypertension and iron-deficiency anaemia. CONCLUSIONS: The drivers of unplanned ACSC admission rates are complex and include population, practice and hospital factors. The importance of these varies markedly across conditions suggesting that multifaceted interventions are required to avoid hospital admissions and reduce costs. Several of the most important drivers of admissions are largely beyond the control of GPs. However, strategies to improve primary care continuity and avoid unnecessary short-stay admissions could lead to improved efficiency. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12875-017-0638-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5445441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54454412017-05-30 How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study Busby, John Purdy, Sarah Hollingworth, William BMC Fam Pract Research Article BACKGROUND: Reducing unplanned hospital admissions is a key priority within the UK and other healthcare systems, however it remains uncertain how this can be achieved. This paper explores the relationship between unplanned ambulatory care sensitive condition (ACSC) admission rates and population, general practice and hospital characteristics. Additionally, we investigated if these factors had a differential impact across 28 conditions. METHODS: We used the English Hospital Episode Statistics to calculate the number of unplanned ACSC hospital admissions for 28 conditions at 8,029 general practices during 2011/12. We used multilevel negative binomial regression to estimate the influence of population (deprivation), general practice (size, access, continuity, quality, A&E proximity) and hospital (bed availability, % day cases) characteristics on unplanned admission rates after adjusting for age, sex and chronic disease prevalence. RESULTS: Practices in deprived areas (at the 90th centile) had 16% (95% confidence interval: 14 to 18) higher admission rates than those in affluent areas (10th centile). Practices with poorer care continuity (9%; 8 to 11), located closest to A&E (8%; 6 to 9), situated in areas with high inpatient bed availability (14%; 10 to 18) or in areas with a larger proportion of day case admissions (17%; 12 to 21) had more admissions. There were smaller associations for primary care access, clinical quality, and practice size. The strength of associations varied by ACSC. For example, deprivation was most strongly associated with alcohol related diseases and COPD admission rates, while continuity of primary care was most strongly associated with admission rates for chronic diseases such as hypertension and iron-deficiency anaemia. CONCLUSIONS: The drivers of unplanned ACSC admission rates are complex and include population, practice and hospital factors. The importance of these varies markedly across conditions suggesting that multifaceted interventions are required to avoid hospital admissions and reduce costs. Several of the most important drivers of admissions are largely beyond the control of GPs. However, strategies to improve primary care continuity and avoid unnecessary short-stay admissions could lead to improved efficiency. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12875-017-0638-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-25 /pmc/articles/PMC5445441/ /pubmed/28545412 http://dx.doi.org/10.1186/s12875-017-0638-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Busby, John Purdy, Sarah Hollingworth, William How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title | How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title_full | How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title_fullStr | How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title_full_unstemmed | How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title_short | How do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
title_sort | how do population, general practice and hospital factors influence ambulatory care sensitive admissions: a cross sectional study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5445441/ https://www.ncbi.nlm.nih.gov/pubmed/28545412 http://dx.doi.org/10.1186/s12875-017-0638-9 |
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