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Title: Exploratory Analysis and Predictive Modeling to Elucidate Old-Age Multimorbidity Trajectories

Citation Type: Dissertation/Thesis

Publication Year: 2022

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Abstract: The study of longitudinal multimorbidity trajectories (MOTRs) in older adults is a growing field of study. Many previously used multimorbidity measurement methods have been cross-sectional in nature, and more studies utilizing longitudinal methods are needed. This population-based study aims to (1) investigate the construction of MOTRs using semi-parametric group based trajectory models (GBTM) employing multiple combinations of comorbidity indices and index score calculation methods, and stratified by sex, (2) generate non-parametric unsupervised machine learning multimorbidity clustering trajectories and compare them to similar GBTM models, and (3) using supervised machine learning classification algorithms, determine the influence of earlylife conditions compared to unsupervised machine learning multimorbidity clustering trajectories on mortality. We found that the least-chronic disease (Escaper) and highest-chronic disease (Frail) MOTRs consistently manifested with all combinations of model types and comorbidity index score calculation methods. Additionally, we found that unsupervised machine learning cluster trajectories had higher performance than GBTM models and may be candidates for implementation in a clinical setting. Futhermore, multimorbidity trajectory membership was more influential in the prediction of mortality than early-life conditions. Although early-life conditions have some effect on the risk of mortality, current disease burden is an appropriate area of focus when the goal is to decrease the risk of mortality in an older adult population. As far as we know, development of longitudinal unsupervised machine learning clustering of chronic disease in older adults using common comorbidity index scores and development of supervised machine learning predictive classification models using MOTRs as features has not been done. This dissertation has contributed to the literature by helping to understand how MOTRs are built, how multimorbidity manifests over time, and how MOTRs influence mortality in an older adult population. Clinicians may use the information and techniques developed here to identify older adult patients on an unhealthy disease trajectory with the goal of a possible intervention to shift the patient to a healthier trajectory.

Url: https://www.proquest.com/docview/2776050849?pq-origsite=gscholar&fromopenview=true

Url: https://www.proquest.com/docview/2776050849/abstract/EB10C0E4D9D94E6CPQ/1?accountid=14586

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Authors: Newman, Michael George

Institution: The University of Utah

Department: Public Health

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Pages: 1-102

Data Collections: IPUMS NHGIS

Topics: Aging and Retirement, Fertility and Mortality, Health

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