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dc.contributor.authorCongdon, Pen_US
dc.date.accessioned2016-08-26T12:30:55Z
dc.date.available2006-11-16en_US
dc.date.issued2006-11-16en_US
dc.date.submitted2016-08-03T14:16:05.983Z
dc.identifier.other10.1186/1476-072X-5-50
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/14873
dc.descriptionThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractBACKGROUND: Appropriate management of care--for example, avoiding unnecessary attendances at, or admissions to, hospital emergency units when they could be handled in primary care--is an important part of health strategy. However, some variations in these outcomes could be due to genuine variations in health need. This paper proposes a new method of explaining variations in hospital utilisation across small areas and the general practices (GPs) responsible for patient primary care. By controlling for the influence of true need on such variations, one may identify remaining sources of excess emergency attendances and admissions, both at area and practice level, that may be related to the quality, resourcing or organisation of care. The present paper accordingly develops a methodology that recognises the interplay between population mix factors (health need) and primary care factors (e.g. referral thresholds), that allows for unobserved influences on hospitalisation usage, and that also reflects interdependence between hospital outcomes. A case study considers relativities in attendance and admission rates at a North London hospital involving 149 small areas and 53 GP practices. RESULTS: A fixed effects model shows variations in attendances and admissions are significantly related (positively) to area and practice need, and nursing home patients, and related (negatively) to primary care access and distance of patient homes from the hospital. Modelling the impact of known factors alone is not sufficient to produce a satisfactory fit to the observations, and random effects at area and practice level are needed to improve fit and account for overdispersion. CONCLUSION: The case study finds variation in attendance and admission rates across areas and practices after controlling for need, and remaining differences between practices may be attributable to referral behaviour unrelated to need, or to staffing, resourcing, and access issues. In managerial terms, the analysis points to the utility of formal statistical analysis of hospitalisation rates as a prelude to non-statistical investigation of primary care resourcing and organisation. For example, there may be implications for the location of staff involved in community management of chronic conditions; health managers may also investigate whether some practices have unusual populations (homeless, asylum seekers, students) that explain different hospital use patterns.en_US
dc.format.extent50 - ?en_US
dc.languageengen_US
dc.language.isoenen_US
dc.relation.ispartofInt J Health Geogren_US
dc.rightsCreative Commons Attribution License
dc.subjectAged, 80 and overen_US
dc.subjectFamily Practiceen_US
dc.subjectFrail Elderlyen_US
dc.subjectHumansen_US
dc.subjectLondonen_US
dc.subjectModels, Theoreticalen_US
dc.subjectPatient Admissionen_US
dc.subjectPractice Patterns, Physicians'en_US
dc.titleModelling multiple hospital outcomes: the impact of small area and primary care practice variation.en_US
dc.typeArticle
dc.rights.holder2006 Congdon; licensee BioMed Central Ltd.
dc.identifier.doi10.1186/1476-072X-5-50en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/17109747en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume5en_US
dcterms.dateAccepted2006-11-16en_US


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