Early Adversity and Health Outcomes Tests of the Model

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The association of early adversity with adverse health outcomes is clear. Not only does outright abuse impact health across the life span, but low SES in childhood and a harsh early family environment also affect health. To this point, we have identified socioemotional skills, chronic negative affective states, and health behaviors as among the mediators to health outcomes. As yet, however, no tests of the entire model have been presented to suggest that these are indeed the routes by which early environment has adverse health effects.

We accordingly undertook several collaborative studies with the Coronary Artery Risk Development in Young Adults Study (CARDIA), an ongoing, prospective, epidemi-ologic investigation of risk factors for coronary artery disease involving more than 3000 participants at four different recruitment sites (Lehman et al, 2005). The samples were approximately evenly balanced between African American and white participants and between men and women. At the initial examination, participants were between the ages of 18 and 25. There have been five follow-up studies since that time, most recently at year 15 (2000-2001). Our investigations with CARDIA used structural equation modeling to determine whether the model in Fig. 36.1 can account for individual differences in adult metabolic functioning, C-reactive protein (CRP), and blood pressure.

Metabolic functioning is a complex of risk factors for coronary artery disease and diabetes and is typically defined by fasting glucose, cholesterol, triglycerides, and abdominal obesity, among other indicators. High levels of these variables contribute to metabolic syndrome, which is prognostic for heart disease, diabetes, inflammatory disorders, and all-cause mortality (see Chapter 46). The prevalence of metabolic syndrome in the United States is approximately 22% (McEwen and Seeman, 1999), making it an important contributor to chronic illness.

We had included an assessment of early family environment during the year 15 CARDIA data collection and tested our model on this sample with a composite index of indicators of metabolic functioning as an outcome variable. Socioemotional functioning was assessed by depression/hostility and the positivity/negativity of social contacts. The model fit the data very well, with early family environment strongly related to socioemotional functioning, which in turn, was significantly related to metabolic functioning (Lehman et al, 2005). When each of the race-sex subgroups was examined separately, the model continued to be an acceptable fit. These findings suggest that early environment is significantly related to dysregulation in socioe-motional functioning, which in turn leads to alterations in metabolic functioning.

A second investigation related the model to CRP (Taylor et al, 2006b). CRP is a biomarker of inflammatory processes which has been reliably related to depression (e.g., Suarez, 2004) and to enhanced risk for cardiovascular disease (King et al, 2004), among other diseases. As was true of metabolic functioning, the model was a good fit to the data, suggesting that the model helps to explain differences in CRP. Since CRP is related to risks for both mental and physical health disorders, it may be important for understanding the comorbidities observed between mental and physical health disorders (e.g., Martin et al, 1995).

In a third investigation (Lehman et al, 2009), we related the model to blood pressure and to changes in blood pressure across the longitudinal occasions with the CARDIA sample. We found that a harsh family environment was related to negative emotions and to obesity, which in turn predicted blood pressure as well as change in blood pressure. Low childhood SES directly predicted change in systolic blood pressure as well. The strength of these pathways did not vary by race or gender. Thus, the findings suggest that socioemotional factors contribute to biological mechanisms that may underlie the impact of early family environment on the development of elevated blood pressure.

Two important caveats deserve mention. First, the effects revealed in these tests of the model were modest in size. One reason is that genetic factors are strong contributors to these outcomes, and they could not be measured in this data set. Second, the fact that participants reconstructed their early family environment and that these studies were retrospective rather than prospective raises the possibility that negative emotions themselves color reconstruction of family environment. Accordingly, for all three of these investigations, we evaluated an alternative model that gave chronic negative affect causal priority in the model to see if it affected reconstruction of family environment. In all three cases, this model was a significantly poorer fit to the data. Moreover, there is parallel evidence from studies relating documented childhood maltreatment to adverse mental and physical health outcomes (Danese et al, 2007, 2008). As such, we conclude that although negative affect may color how people regard their families, reconstructive biases do not account for the relation of early family environment to adverse health outcomes.

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