James Hogg , Susanna Cramb , Jessica Cameron , Peter Baade , Kerrie Mengersen
{"title":"Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model","authors":"James Hogg , Susanna Cramb , Jessica Cameron , Peter Baade , Kerrie Mengersen","doi":"10.1016/j.healthplace.2024.103295","DOIUrl":null,"url":null,"abstract":"<div><p>This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product — representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.</p></div>","PeriodicalId":49302,"journal":{"name":"Health & Place","volume":"89 ","pages":"Article 103295"},"PeriodicalIF":3.8000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1353829224001230/pdfft?md5=a63d0027332b84f96802a584cc14140b&pid=1-s2.0-S1353829224001230-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health & Place","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1353829224001230","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product — representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.