Feasibility of Classifying Life Stages and Searching for the Determinants: Results from the Medical Expenditure Panel Survey 1996-2011

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.

Keywords: life stages; medical expenditure panel survey; principal component analysis; principal components; stable stages; stages of transition.

Figure 1

Flowchart of data linkage, data processing, and feature selection with the Medical Expenditure Panel Survey (MEPS) 1996–2011.

Figure 2

The numbers of population and variances of principal components (PCs) by age. Note: the area under the first lowest curve represents the variance of the first principal component (PC1); the area between the first and second lowest curves represents the variance of the second PC (PC2); this principle applies to other areas under curves. There are 525 PCs in the graph. The upper black solid line represents the numbers of total populations by age; the lower represents the numbers of female populations. The spikes of population numbers at age 85 are due to the top censoring of age beginning in 2000.

Figure 3

Proportions of variances explained by the first 20 principal components (PCs). Note: red line: 1% of total variance; 525 PCs in total.

Figure 4

Mean values of the first to the eighth principal components (PCs) by age. Note: 95% CIs of the PCs are the ranges of the same colors as the PCs. The colors and corresponding numbers of PCs are labeled on the graph.

Figure 5

Mean values of the 1st and the 9th–16th principal components (PCs) by age. Note: 95% CIs of the PCs are the ranges of the same colors as the PCs. The color of first PC is navy blue. The colors and corresponding numbers of PCs are labeled on the graph.

Figure 6

The pairwise comparisons of the first principal component by age groups. The value in each cell is the difference of PC1 between two age groups. The differences that are not statistically significant (p values adjusted for multiple comparisons based on the Benjamini–Hochberg method at 0.05) were left blank. The gray areas are the groups of consecutive ages with less than 5% significant differences in pairwise comparisons.

Figure 7

The pairwise comparisons of the second principal component (PCs) by age groups. The value in each cell is the difference of PC1 between two age groups. The differences that are not statistically significant (p values adjusted for multiple comparisons based on the Benjamini–Hochberg method at 0.05) were left blank. The gray areas are the groups of consecutive ages with less than 5% significant differences in pairwise comparisons.