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Profile development in population studies: clustering and latent class methodologies

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dc.contributor.advisor Purtill, Helen
dc.contributor.advisor Walsh, Cathal Dominic O'Neill, Aoife 2021-03-31T09:03:09Z 2021-03-31T09:03:09Z 2019
dc.description peer-reviewed en_US
dc.description.abstract Identifying subgroups within a population can provide important insights to decision makers. When concerned with health data, identifying profiles of individuals who share similar characteristics may inform targeted treatment or education. Categorical data are often prevalent in large population studies. Therefore statistical profiling methods capable of handling these data are required. In this dissertation, TwoStep cluster analysis (TSCA) and latent class analysis (LCA), were used to identify cross-sectional profiles, while latent transition analysis (LTA) was used to identify profiles longitudinally. Data from two Irish longitudinal studies; Growing Up in Ireland (GUI) and The Irish Longitudinal Study on Ageing (TILDA), and one European study of Rheumatology health professionals (HPs), were examined. TSCA was used to examine activity behaviours in 9-year-old Irish children from GUI. Cohesive activity profiles, associated with weight status, were found for boys but not for girls. LCA identified three classes of rheumatology HPs; ‘Traditional’, ‘Reluctant’ and ‘Early Adopters’, based on their methods of measuring physical activity in their patients. Next, a LCA model of risk behaviours in older adults, was developed from a theoretical model of biopsychosocial behaviours, using TILDA data. The analysis identified four classes; ‘Low Risk’, ‘Physical Health Risk’, ‘Mental Health Risk’, and ‘High Risk’, and examined associations with pain development. A traditional classify-analyse approach and a model-based distal outcome approach, were compared. The LTA methodologies were extended by developing a bootstrap approach to calculate 95% confidence intervals for the odds ratios generated by the LTA with covariates model, multiple random seeds were generated to identify the maximum likelihood, and sample weights were incorporated into the analysis. LTA examined changes in pain class over time, using in TILDA data, and investigated how transitions between classes where related to biopsychosocial variables and healthcare utilisation. These results add new findings to the literature and extend the latent class method ologies applied to population data. These findings have important implications for the identification, and potential moderation, of a range of risk factors, as well as potential to inform targeted treatment or education programmes. en_US
dc.language.iso eng en_US
dc.publisher University of Limerick en_US
dc.subject population studies en_US
dc.subject health data en_US
dc.title Profile development in population studies: clustering and latent class methodologies en_US
dc.type info:eu-repo/semantics/doctoralThesis en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.supercollection ul_theses_dissertations en_US
dc.contributor.sponsor HRB en_US
dc.relation.projectid RL2013/04 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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