衡量健康素养,为解决健康不公平问题的行动提供信息:基于澳大利亚全国健康素养调查的聚类分析方法。

Christina Cheng, Shandell Elmer, Roy Batterham, Melanie Hawkins, Richard H Osborne
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摘要

背景:测量健康素养可为干预措施提供信息,以解决健康不平等问题。本研究使用聚类分析来检查健康素养数据,以确定它是否能提供比标准描述性分析更有洞察力的信息,从而更好地为干预措施的制定提供依据:本研究利用澳大利亚全国健康调查(2018年)的数据,比较了两个州--新南威尔士州(NSW)和维多利亚州--从健康素养问卷(HLQ)中得出的描述性分析和聚类分析结果。根据健康素养问卷的九个量表得分,采用沃德联系法进行了分层聚类分析:新南威尔士州和维多利亚州的受访者人数分别为 1018 人和 923 人。两个州的九个 HLQ 量表全样本平均分相似。然而,聚类分析发现,新南威尔士州有 11 个聚类,维多利亚州有 12 个聚类。虽然每个州都有 6 个聚类呈现出相似的健康素养模式,但新南威尔士州和维多利亚州分别有 5 个和 6 个聚类呈现出独特的健康素养模式:结论:研究结果表明,描述性分析只能提供一个概况,可能会导致一刀切的干预措施。聚类分析所得出的不同亚群的健康素养模式为采取有针对性的行动提高健康公平性铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring health literacy to inform actions to address health inequities: a cluster analysis approach based on the Australian national health literacy survey.

Background: Measuring health literacy can inform interventions to address health inequities. This study used cluster analysis to examine health literacy data to determine if it can provide more insightful information than standard descriptive analysis to better inform intervention development.

Methods: Using data from the Australian National Health Survey (2018), this study compared descriptive analysis and cluster analysis results of two states-New South Wales (NSW) and Victoria-generated from the Health Literacy Questionnaire (HLQ). Based on the nine scale scores of the HLQ, a hierarchical cluster analysis using Ward's method for linkage was undertaken.

Results: The number of NSW and Victoria respondents was 1018 and 923, respectively. The nine HLQ scale full sample mean scores from both states were similar. However, the cluster analyses identified 11 clusters for NSW and 12 clusters for Victoria. While six clusters from each state presented similar health literacy patterns, five and six clusters from NSW and Victoria, respectively, displayed unique health literacy patterns.

Conclusions: The results demonstrate that descriptive analysis only provides an overview and may lead to one-size-fits-all interventions. The varying health literacy patterns among subgroups resulting from the cluster analysis pave the way to inform tailored actions to improve health equity.

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