Abstract
BACKGROUND: Life stages are not clearly defined and significant determinants for the identification of stages are not discussed. This study aims to test a data-driven approach to define stages and to identify the major determinants. METHODS: This study analyzed the data on the Medical Expenditure Panel Survey interviewees from 1996 to 2011 in the United States. This study first selected features with the Spearman's correlation to remove redundant variables and to increase computational feasibility. The retained 430 variables were log transformed, if applicable. Sixty-four nominal variables were replaced with 164 binominal variables. This led to 525 variables that were available for principal component analysis (PCA). Life stages were proposed to be periods of ages with significantly different values of principal components (PCs). RESULTS: After retaining subjects followed throughout the panels, 244,089 were eligible for PCA, and the number of civilians was estimated to be 4.6 billion. The age ranged from 0 to 90 years old (mean = 35.88, 95% CI = 35.67-36.09). The values of the first PC were not significant from age of 6 to 13, 30 to 41, 46 to 60, and 76 to 90 years (adjusted p > 0.5), and the major determinants were related to functional status, employment, and poverty. CONCLUSION: Important stages and their major determinants, including the status of functionality and cognition, income, and marital status, can be identified. Identifying stages of stability or transition will be important for research that relies on a research population with similar characteristics to draw samples for observation or intervention. CONTRIBUTION: This study sets an example of defining stages of transition and stability across ages with social and health data. Among all available variables, cognitive limitations, income, and poverty are important determinants of these stages.