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澳大利亚维多利亚州门诊护理敏感条件的小区域分析。

Small area analysis of ambulatory care sensitive conditions in Victoria, Australia.

机构信息

Health Intelligence Unit, Prevention and Population Health, Melbourne, Victoria, Australia.

出版信息

Popul Health Manag. 2013 Jun;16(3):190-200. doi: 10.1089/pop.2012.0047. Epub 2013 Feb 13.

DOI:10.1089/pop.2012.0047
PMID:23405877
Abstract

Ambulatory care sensitive conditions (ACSCs) are used as a measure of access to primary health care. The purpose of this study was to identify factors associated with variation in ACSC admissions at a small area level in Victoria, Australia. The study was ecologic, using Victorian Primary Care Partnerships (PCPs) as the unit of analysis. Data sources were the Victorian Admitted Episodes Dataset, census data from the Australian Bureau of Statistics, and the Victorian Population Health Survey. Age- and sex-adjusted total ACSC admission rates were calculated, and weighted least squares multiple linear regression was used to examine the associations of total ACSC admission rates by various predictor variables. Key variables were categorized into 1 of 4 framework components for analyzing access and use of health care services: predisposing, enabling, need, or structural. Enabling characteristics explained 61.70% of the variation in ACSC admission rates across PCPs. Socioeconomic characteristics (income, education) and percentage with poor self-rated health were important factors in explaining variations in ACSC admissions at a small area-level [R(2)=0.77]. Community-level variables differentially affect access to primary health care, with significant variation by socioeconomic status. This analytical approach will assist researchers to identify community-level predicators of access across populations at locations, including factors that may be affected by policy change.

摘要

门诊护理敏感条件 (ACSCs) 被用作评估初级卫生保健可及性的指标。本研究旨在确定在澳大利亚维多利亚州小区域水平上与 ACSC 入院率变化相关的因素。该研究采用生态研究设计,以维多利亚初级保健合作伙伴关系 (PCPs) 为分析单位。数据来源包括维多利亚州住院记录数据集、澳大利亚统计局的人口普查数据以及维多利亚州人口健康调查。计算了年龄和性别调整后的总 ACSC 入院率,并使用加权最小二乘多元线性回归分析了各种预测变量与总 ACSC 入院率之间的关联。关键变量分为分析卫生保健服务获取和利用的 4 个框架组成部分之一:倾向因素、促成因素、需求因素或结构因素。在 PCPs 中,促成因素解释了 ACSC 入院率变化的 61.70%。社会经济特征(收入、教育)和自评健康状况不佳的比例是解释 ACSC 入院率在小区域水平上差异的重要因素[R(2)=0.77]。社区水平变量对初级卫生保健的可及性有不同的影响,社会经济地位差异显著。这种分析方法将帮助研究人员确定不同人群在不同地点的初级卫生保健获取的社区水平预测因子,包括可能受政策变化影响的因素。

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