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Hospital admissions for asthma, diabetes, and COPD: is there an association with practice nurse staffing? A cross sectional study using routinely collected data

Hospital admissions for asthma, diabetes, and COPD: is there an association with practice nurse staffing? A cross sectional study using routinely collected data
Hospital admissions for asthma, diabetes, and COPD: is there an association with practice nurse staffing? A cross sectional study using routinely collected data
Background: Delivering good quality primary care for patients with chronic conditions has the potential to reduce non-elective hospital admissions. Practice nurse staffing levels in England have been linked to attainment of general practice performance targets for some chronic conditions. The aim of this study was to examine whether practice nurse staffing level is similarly associated with non-elective hospital admissions in three clinical areas: asthma, Chronic Obstructive Pulmonary Disease (COPD) and diabetes.

Methods: This observational study used cross sectional analysis of routinely collected data. Hospital admissions data for the period 2005-2006 (for asthma, COPD and diabetes) were linked with a database of practice characteristics, nurse staffing data and data on population characteristics for the same period. Statistical modelling explored the relationship between non-elective hospital admission rates for the three conditions and the list size per full time equivalent (FTE) practice nurse.

Results: Higher practice nurse staffing levels were significantly associated with lower rates of admission for asthma (p < 0.001) and COPD (p < 0.001). A similar association was seen for patients with two or more admissions (p < 0.05 for asthma and p < 0.001 for COPD). For diabetes, higher practice nurse staffing level was significantly associated with higher admission rates (p < 0.05), but this association was not significant in case of patients with two or more admissions. Across all models, increasing deprivation was associated with higher admission rates for all conditions.

Conclusions: The inconsistent relationship between nurse staffing and patient outcomes across the different conditions and the fact that for diabetes the relationship between staffing and outcomes was in a different direction from the association between staffing and care quality, highlights the need to avoid making a simple causal interpretation of these findings and reduces the possible confidence in such conclusions. There is a need for more research into the organisation and delivery of diabetes care services in general practice, preferably using patient level data; in order to better understand the impact of the different staffing configurations on patient outcomes.
1472-6963
276
Griffiths, Peter
ac7afec1-7d72-4b83-b016-3a43e245265b
Murrells, Trevor
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Dawoud, Dalia
c4a6a9f2-6de8-4353-a349-cdf6aa8be1ea
Jones, Simon
f5d66e16-2c8e-4d48-ab97-0715a6e85c46
Griffiths, Peter
ac7afec1-7d72-4b83-b016-3a43e245265b
Murrells, Trevor
9a57589a-d893-415c-8c3d-8b25d052f42c
Dawoud, Dalia
c4a6a9f2-6de8-4353-a349-cdf6aa8be1ea
Jones, Simon
f5d66e16-2c8e-4d48-ab97-0715a6e85c46

Griffiths, Peter, Murrells, Trevor, Dawoud, Dalia and Jones, Simon (2010) Hospital admissions for asthma, diabetes, and COPD: is there an association with practice nurse staffing? A cross sectional study using routinely collected data. BMC Health Services Research, 10, 276. (doi:10.1186/1472-6963-10-276). (PMID:2955649)

Record type: Article

Abstract

Background: Delivering good quality primary care for patients with chronic conditions has the potential to reduce non-elective hospital admissions. Practice nurse staffing levels in England have been linked to attainment of general practice performance targets for some chronic conditions. The aim of this study was to examine whether practice nurse staffing level is similarly associated with non-elective hospital admissions in three clinical areas: asthma, Chronic Obstructive Pulmonary Disease (COPD) and diabetes.

Methods: This observational study used cross sectional analysis of routinely collected data. Hospital admissions data for the period 2005-2006 (for asthma, COPD and diabetes) were linked with a database of practice characteristics, nurse staffing data and data on population characteristics for the same period. Statistical modelling explored the relationship between non-elective hospital admission rates for the three conditions and the list size per full time equivalent (FTE) practice nurse.

Results: Higher practice nurse staffing levels were significantly associated with lower rates of admission for asthma (p < 0.001) and COPD (p < 0.001). A similar association was seen for patients with two or more admissions (p < 0.05 for asthma and p < 0.001 for COPD). For diabetes, higher practice nurse staffing level was significantly associated with higher admission rates (p < 0.05), but this association was not significant in case of patients with two or more admissions. Across all models, increasing deprivation was associated with higher admission rates for all conditions.

Conclusions: The inconsistent relationship between nurse staffing and patient outcomes across the different conditions and the fact that for diabetes the relationship between staffing and outcomes was in a different direction from the association between staffing and care quality, highlights the need to avoid making a simple causal interpretation of these findings and reduces the possible confidence in such conclusions. There is a need for more research into the organisation and delivery of diabetes care services in general practice, preferably using patient level data; in order to better understand the impact of the different staffing configurations on patient outcomes.

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Published date: 21 September 2010
Organisations: Health Sciences

Identifiers

Local EPrints ID: 168507
URI: http://eprints.soton.ac.uk/id/eprint/168507
ISSN: 1472-6963
PURE UUID: a720d0ad-5ffe-4e46-934e-0cf159ef6f13
ORCID for Peter Griffiths: ORCID iD orcid.org/0000-0003-2439-2857

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Date deposited: 30 Nov 2010 11:43
Last modified: 14 Mar 2024 02:56

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Author: Peter Griffiths ORCID iD
Author: Trevor Murrells
Author: Dalia Dawoud
Author: Simon Jones

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