Effects of Socioeconomic Status and County-Level Income Inequality on Adult C-Reactive Protein

• In Event: Socioeconomic Status Across the Life Course as a Predictor of Health and Parenting Outcomes

Abstract

Recent meta-analyses have shown that as socioeconomic status (SES) increases, markers of systemic inflammation such as high sensitivity C-reactive protein (hsCRP) and interleukin-6 (IL-6) that are associated with negative health outcomes decline in both children and adults (Muscatell et al. 2020, Milaniak & Jaffee 2019). These studies typically focus on individual- or family-level SES in childhood or adulthood. Markers of relative deprivation such as income inequality are also powerful predictors of health outcomes (Pickett & Wilkinson 2015) and some studies have shown that social gradients in health are more pronounced in counties or states with greater income inequality (Wilkinson & Pickett 2008, Kennedy et al. 1998). Thus, the goal of the present study was to test whether the association between SES and a biomarker associated with poor health (hsCRP) was magnified in the context of heightened income inequality within the United States. Moreover, we aimed to test the relative strength of associations between childhood SES versus adult SES and adult hsCRP and whether either of these associations were moderated by county-level income inequality. To test these hypotheses, we used data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth).

Add Health is a study of a nationally representative sample who were in grades 7 to 12 in 1994-95 at Wave 1 when data were collected from 90,118 students. Add Health respondents were followed up from 1995-1996 to 2016-2018 (Wave 5; n = 12,300) by which time participants ranged in age from 33 to 43 years. A subsample of Wave 5 participants (n = 5,381) provided biological data, including hsCRP assayed from venous blood draws. Childhood SES was measured from parent reports of household income and education at Wave 1. Adult SES was measured as the respondents’ income and education at Wave 5. County-level income inequality was measured with the GINI coefficient. To test the relative predictive power of childhood SES, adult SES, and county-level income inequality on adult hsCRP, we regressed hsCRP on all SES and income inequality measures, controlling for a range of covariates measured at Wave 5 in the in-home assessment (gender, BMI, diabetes status, recent inflammatory disease). We also tested whether county-level income inequality moderated effects of child or adult SES on adult hsCRP. Analyses were adjusted for the multistage sampling design and nonresponse.

Table 1 presents descriptive statistics for key analysis variables. Correlations among key study variables were small, but significant. As shown in Table 2, adult SES was significantly associated with hsCRP, but childhood SES and county-level income inequality were not. We did not find support for our hypothesis that the association between adult SES and hsCRP would be steeper in counties with more (versus less) income inequality. Subsequent research may examine effects of individual SES and income inequality on other inflammatory biomarkers such as IL-6, TNF- α, or CMV.

Authors

• George Lin, University of Pennsylvania
  Presenting Author

• Sara R Jaffee, University of Pennsylvania