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Methods: I analyzed data from 5,196 participants of the National Longitudinal Survey of Youth. Upon turning 40 and again when 50, respondents indicated whether they had ever been diagnosed with any of seven chronic health conditions. A latent transition analysis classified respondents based on these health conditions. Number of latent statuses was determined using Bayseian Information Criterion (BIC), other fit indices, and model interpretability. Respondent morbidity status became the dependent variable in a multinomial regression. Model predictors were indicators of life course socioeconomic conditions. They included measures of parental and respondent education, life course income, wealth at mid-life, home ownership, race and ethnicity, and other control variables with demonstrable associations to health, including smoking, alcohol, and body mass index.

Results: Fit indices identified 4-statuses, a small multi-morbid status predominantly associated with heart and lung conditions, two moderately sized statuses associated with arthritis and hypertension, respectively, and one large status whose members tended to report no chronic health conditions. Income, wealth, and education were significantly related to morbidity statuses at two time points Implications: These findings support a link between life course socioeconomic conditions and accrual of multiple medical conditions. Understanding the nature of these relationships is relevant for micro and macro-practice. Greater attunement to the link between health issues and economic and social adversity becomes critical to assessment and service coordination. Further, macro-practitioners could sharpen community level needs assessment and target macro-level interventions to achieve broader community health benefits.