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本文引用的文献

1
The Acute Asthma Severity Assessment Protocol (AASAP) study: objectives and methods of a study to develop an acute asthma clinical prediction rule.急性哮喘严重程度评估方案(AASAP)研究:旨在开发急性哮喘临床预测规则的研究目标和方法。
Emerg Med J. 2012 Jun;29(6):444-50. doi: 10.1136/emj.2010.110957. Epub 2011 May 17.
2
Public hospital bed crisis: too few or too misused?公立医院床位危机:是数量太少还是使用不当?
Aust Health Rev. 2010 Aug;34(3):317-24. doi: 10.1071/AH09821.
3
Admissions to Canadian hospitals for acute asthma: a prospective, multicentre study.加拿大急性哮喘住院患者:一项前瞻性、多中心研究。
Can Respir J. 2010 Jan-Feb;17(1):25-30. doi: 10.1155/2010/178549.
4
Corticosteroids for acute chest syndrome in children with sickle cell disease: variation in use and association with length of stay and readmission.皮质类固醇治疗镰状细胞病儿童急性胸部综合征:使用的差异与住院时间和再入院的关系。
Am J Hematol. 2010 Jan;85(1):24-8. doi: 10.1002/ajh.21565.
5
Management of acute asthma in adults in the emergency department: nonventilatory management.急诊科成人急性哮喘的管理:非通气管理
CMAJ. 2010 Feb 9;182(2):E55-67. doi: 10.1503/cmaj.080072. Epub 2009 Oct 26.
6
Readmission in children's emergency care: an analysis of hospital episode statistics.儿童急诊再入院:医院病例统计分析。
Arch Dis Child. 2010 May;95(5):341-6. doi: 10.1136/adc.2009.163261. Epub 2009 Sep 21.
7
Inpatient length of stay: a finite mixture modeling analysis.住院患者住院时间:有限混合模型分析。
Eur J Health Econ. 2010 Apr;11(2):119-26. doi: 10.1007/s10198-009-0153-6. Epub 2009 May 12.
8
Excess cost and length of stay associated with voluntary patient safety event reports in hospitals.医院中与自愿性患者安全事件报告相关的额外成本和住院时间。
Am J Med Qual. 2009 Jan-Feb;24(1):53-60. doi: 10.1177/1062860608327610.
9
Assessment of severity measures for acute asthma outcomes: a first step in developing an asthma clinical prediction rule.急性哮喘结局严重程度测量的评估:制定哮喘临床预测规则的第一步。
Am J Emerg Med. 2008 May;26(4):473-9. doi: 10.1016/j.ajem.2007.05.026.
10
Burden of asthma in the hospital setting: an Australian analysis.医院环境中哮喘的负担:一项澳大利亚的分析。
Int J Clin Pract. 2007 Nov;61(11):1884-8. doi: 10.1111/j.1742-1241.2007.01559.x. Epub 2007 Sep 14.

2001-2006 年伦敦医院哮喘住院时间:人口统计学、诊断和时间因素。

Asthma length of stay in hospitals in London 2001-2006: demographic, diagnostic and temporal factors.

机构信息

School of Public Health, University of Ghana, Accra, Ghana.

出版信息

PLoS One. 2011;6(11):e27184. doi: 10.1371/journal.pone.0027184. Epub 2011 Nov 2.

DOI:10.1371/journal.pone.0027184
PMID:22073282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3206938/
Abstract

Asthma is a condition of significant public health concern associated with morbidity, mortality and healthcare utilisation. This study identifies key determinants of length of stay (LOS) associated with asthma-related hospital admissions in London, and further explores their effects on individuals. Subjects were primarily diagnosed and admitted for asthma in London between 1(st) January 2001 and 31(st) December 2006. All repeated admissions were treated uniquely as independent cases. Negative binomial regression was used to model the effect(s) of demographic, temporal and diagnostic factors on the LOS, taking into account the cluster effect of each patient's hospital attendance in London. The median and mean asthma LOS over the period of study were 2 and 3 days respectively. Admissions increased over the years from 8,308 (2001) to 10,554 (2006), but LOS consistently declined within the same period. Younger individuals were more likely to be admitted than the elderly, but the latter significantly had higher LOS (p<0.001). Respiratory related secondary diagnoses, age, and gender of the patient as well as day of the week and year of admission were important predictors of LOS. Asthma LOS can be predicted by socio-demographic factors, temporal and clinical factors using count models on hospital admission data. The procedure can be a useful tool for planning and resource allocation in health service provision.

摘要

哮喘是一种严重的公共卫生问题,与发病率、死亡率和医疗保健利用有关。本研究确定了与伦敦哮喘相关住院治疗相关的住院时间(LOS)的主要决定因素,并进一步探讨了它们对个体的影响。研究对象主要是在 2001 年 1 月 1 日至 2006 年 12 月 31 日期间在伦敦被诊断和收治的哮喘患者。所有重复入院均被视为独立病例进行单独处理。负二项回归用于对人口统计学、时间和诊断因素对 LOS 的影响进行建模,同时考虑到每位患者在伦敦住院的聚类效应。研究期间,哮喘 LOS 的中位数和平均值分别为 2 天和 3 天。入院人数逐年增加,从 2001 年的 8308 人增加到 2006 年的 10554 人,但同期 LOS 持续下降。年轻人比老年人更有可能入院,但后者的 LOS 明显更高(p<0.001)。与 LOS 相关的次要诊断、患者的年龄和性别以及入院日期和年份是 LOS 的重要预测因素。使用住院数据的计数模型,可以根据社会人口统计学因素、时间和临床因素预测哮喘 LOS。该程序可以成为医疗服务规划和资源分配的有用工具。