Lifetime Trajectories and Drivers of Socioeconomic Health Disparities

Evolution of Aging-Related Chronic Diseases over the Life Cycle

Notes: This figure illustrates the share of observations with varying numbers of aging-related chronic diseases across ages using pooled observations from an unbalanced panel data set. The sample covers ages 18–80. The number of chronic diseases refers to the count of conditions an individual currently has or has previously had at a given age. Details on the list of chronic diseases are provided in Table 2. Different colors (gray shades in black and white version) represent different numbers of chronic diseases, with the lowest at the bottom and the highest at the top. The proportion of the color/shade at each age is the share of observations at this age.

Life Cycle Profile of ALI, CDI, and Three-Year Mortality

Notes: This figure presents the trajectories of ALI, CDI, and three-year mortality rate over the life cycle using pooled observations from an unbalanced panel data set. The sample covers ages 18–80. Due to limitations in death record availability, the number of observations for three-year mortality rate is smaller than for ALI and CDI (see Section III.C for details). For CDI, the figure (and subsequent figures that stratify by gender and education) follows the corresponding figures in Danesh et al. (2024). In our setting, to ensure comparability between CDI and ALI, we rescale the two indexes using a min–max scaling approach. We apply the lpoly smoothing method, a kernel-weighted local polynomial regression, to smooth the trajectories of ALI, CDI, and three-year mortality. Specifically, we use the Epanechnikov kernel with a zero-degree polynomial, which minimizes noise while preserving the underlying data pattern. The 95 percent confidence intervals are shown. The larger variation at younger and older ages reflects the smaller sample sizes in these groups.

Educational Disparities in the ALI by Age and Gender

Notes: This figure presents the trajectories of educational disparities in ALI by age and gender using pooled observations from an unbalanced panel data set. The sample covers ages 18–80. High education is defined as higher vocational or university education according to the Dutch education system, while the rest of the sample is classified as low education (see Section II.B for details). We apply the lpoly smoothing method, a kernel-weighted local polynomial regression of y on x, using the Epanechnikov kernel function with a zero-degree polynomial. The 95 percent confidence intervals are shown. Larger variation at younger and older ages reflects smaller sample sizes in these groups.

Educational Disparities in ALI Across Age and Birth Cohorts

Notes: This figure examines the importance of cohort effects by grouping individuals into ten-year birth cohorts and presenting educational disparities in ALI across ages using pooled observations from an unbalanced panel data set. For example, “80s” refers to participants born in the 1980s. However, the figure still reflects a mix of cohort and age effects because individuals are not followed continuously over time. We apply the lpoly smoothing method, a kernel-weighted local polynomial regression of y on x, using the Epanechnikov kernel function with a zero-degree polynomial. Large variation is observed for the 30s and 90s cohorts due to smaller sample size.

Educational Disparities in the Prevalence of High-Risk Biomarkers Among Individuals Aged Under 30

Notes: This figure illustrates differences in the prevalence of each biomarker-related risk across education groups, segmented by gender. Observations from all three waves are pooled for this analysis. For example, a value of 0.027 for females’ heart rate indicates that 2.7 percent of females have a heart rate above 90 beats per minute (see Table 1 for clinical thresholds for other biomarkers). Among females, the differences in total cholesterol, HbA1c, glucose, and diastolic blood pressure between low- and high-education groups are statistically insignificant at the 10 percent level. Among males, the differences in heart rate and diastolic blood pressure are statistically insignificant at the 10 percent level. All other differences are significant at the 10 percent level.