Christina Cheng, Shandell Elmer, Roy Batterham, Melanie Hawkins, Richard H Osborne
{"title":"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。","authors":"Christina Cheng, Shandell Elmer, Roy Batterham, Melanie Hawkins, Richard H Osborne","doi":"10.1093/pubmed/fdae165","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":94107,"journal":{"name":"Journal of public health (Oxford, England)","volume":" ","pages":"e663-e674"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11637599/pdf/","citationCount":"0","resultStr":"{\"title\":\"Measuring health literacy to inform actions to address health inequities: a cluster analysis approach based on the Australian national health literacy survey.\",\"authors\":\"Christina Cheng, Shandell Elmer, Roy Batterham, Melanie Hawkins, Richard H Osborne\",\"doi\":\"10.1093/pubmed/fdae165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":94107,\"journal\":{\"name\":\"Journal of public health (Oxford, England)\",\"volume\":\" \",\"pages\":\"e663-e674\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11637599/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of public health (Oxford, England)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/pubmed/fdae165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of public health (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/pubmed/fdae165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.