摘要：Objectives. We examined how state characteristics in early life are associated with individual chronic disease later in life. Methods. We assessed early-life state of residence using the first 3 digits of social security numbers from blue- and white-collar workers from a US manufacturing company. Longitudinal data were available from 1997 to 2012, with 305 936 person-years of observation. Disease was assessed using medical claims. We modeled associations using pooled logistic regression with inverse probability of censoring weights. Results. We found small but statistically significant associations between early-state-of-residence characteristics and later life hypertension, diabetes, and ischemic heart disease. The most consistent associations were with income inequality, percentage non-White, and education. These associations were similar after statistically controlling for individual socioeconomic and demographic characteristics and current state characteristics. Conclusions. Characteristics of the state in which an individual lives early in life are associated with prevalence of chronic disease later in life, with a strength of association equivalent to genetic associations found for these same health outcomes. Environments, whether at the level of country, state, county, or neighborhood, account for a meaningful amount of variance in chronic disease. 1–8 Although earlier work was entirely ecological in nature, thus making inferences to individual health outcomes problematic, more recent literature has avoided ecological bias by measuring individual-level outcomes and covariates. 4,9,10 In the United States, state is a level of geography that has varied social characteristics and policy: It is one of the happy incidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country. 11 (p311) The state is also methodologically advantageous as a level of analysis because there is less likely to be severe self-selection bias compared with smaller regions within metropolitan areas, 12 such as neighborhoods, in which wealth and race influence place of residence. 10 Most typically, because of data constraints, only current context is examined, which is inconsistent with life-course theories of the etiology of chronic disease. 13–17 It is possible that the associations found with current measures of the social and economic environment are at least in part the result of impacts of earlier environments that are typically correlated with current context because many people remain geographically stable. 18 Despite social and policy differences in the state context into which people are born and live, little is known about how characteristics may be associated with chronic disease. The majority of recent work has been at smaller levels of geography, with hypotheses focused on local physical and social environments as causal agents. 19–23 In contrast with this, at the state level there are broader macrocharacteristics that are affected by state-specific policies. Which specific characteristics of the state environment may be most correlated with prevalent health later in life is unknown. Also unknown is whether current state characteristics capture all of the same health outcome variance from early-life context and the extent to which individual-level factors may explain any early state context-associated differences. We attempted to establish the relevant time period for measuring exposures in the state context and to determine the importance of the state context in explaining later-life patterns of chronic disease. Specifically, we examined how early-life state characteristics are associated with prevalent hypertension, type 2 diabetes, and ischemic heart disease. We were able to construct these environments through data linkage of individuals in an occupational cohort using the first 3 digits of social security numbers to determine state of early-life residence. 24 Thus, in our analysis, early life is operationalized as the time at which a social security card was issued. We examined the associations between early-life state characteristics and prevalent adult health outcomes. We also fit subsequent models to control for individual demographic data, census region, and current state characteristics. We also examined whether models including individual-level risk factors may in part explain the associations between early-life state context and chronic health conditions.
