The Role of Education in Explaining Racial/Ethnic Allostatic Load Differentials in the United States

Abstract

This study expands on earlier findings of racial/ethnic and education-allostatic load associations by assessing whether racial/ethnic differences in allostatic load persist across all levels of educational attainment. This study used data from four recent waves of the National Health and Nutrition Survey (NHANES). Results from this study suggest that allostatic load differs significantly by race/ethnicity and educational attainment overall, but that the race/ethnicity association is not consistent across education level. Analysis of interactions and education-stratified models suggest that allostatic load levels do not differ by race/ethnicity for individuals with low education; rather, the largest allostatic load differentials for Mexican Americans (p < .01) and non-Hispanic blacks (p < .001) are observed for individuals with a college degree or more. These findings add to the growing evidence that differences in socioeconomic opportunities by race/ethnicity are likely a consequence of differential returns to education, which contribute to higher stress burdens among minorities compared to non-Hispanic whites.

US Military Service and Racial/Ethnic Differences in Cardiovascular Disease: An Analysis of the 2011-2016 Behavioral Risk Factor Surveillance System

“…While differential health risks have been noted in the health disparities literature, they typically work to the disadvantage of non-Hispanic Blacks compared with non-Hispanic Whites. 25 Our findings, however, point to a CVD risk advantage for non-Hispanic Blacks with prior service, compared with non-Hispanic Whites with prior service. This contrasts with other recent findings that, despite having relatively similar health upon enlistment, 26 disparities in cardiovascular risk factors, including hypertension 14,16 and obesity 16 exist among non-Hispanic Black active duty and veteran service members.…”

Sociodemographic, behavioral and genetic determinants of allostatic load in a Swiss population-based study

“…However, we must point out that the majority of studies in this field report a negative association between occupational position and AL for both sexes (Gustafsson et al, 2011). Participants with a low education experienced higher physiological dysregulation as measured by AL, in line with previous research (Howard and Sparks, 2015;Nicod et al, 2014;Seeman et al, 2004) including a study performed in a Swiss population (Nicod et al, 2014). This may be related to several factors such as health-related knowledge on detrimental behaviors (Kenkel, 1991;Nocon et al, 2007), use of health preventive services such as screening (Adler et al, 1993), availability of psychosocial resources such as social support, and better ability to cope with everyday hassles and stressful situations in individuals with high vs. low education (Adler and Snibbe, 2003;Seeman, 1996).…”

Does allostatic load calculation method matter? Evaluation of different methods and individual biomarkers functioning by race/ethnicity and educational level

“…A set of 10 biomarkers, inclusive of all biomarkers available from the physical examination and laboratory components of NHANES, were used to calculate allostatic load scores for each participant (Geronimus et al, ; Howard and Sparks, ; Levine and Crimmins, ; Seeman et al, ; Slade et al, ). Of particular importance is the fact that the lipid panel used to measure triglycerides, one of the variables used in calculating allostatic load, is only administered to the fasting subset of the total NHANES sample (National Center for Health Statistics, ).…”

“…One recent study has shown that factor loadings differ significantly when factor analyses on biomarkers are performed separately for non‐medicated subjects compared to those who have been taking cholesterol, anti‐inflammatory, anti‐hypertensive, and diabetes medications (Booth et al, ). Other studies document overall differences in allostatic load scores based on nativity status (Doamekpor and Dinwiddie, ) and race/ethnicity (Howard and Sparks, ). If interventions can modify the pathways through which allostatic load arises, then perhaps other, more distal, factors could also modify how individual biomarkers are related to the underlying allostatic load factor structure.…”

“…Racial/ethnic and socioeconomic position (SEP) disparities in morbidity and mortality have been documented in numerous comparative studies in the United States and internationally (Adler and Rehkopf, ; Adler and Stewart, ; Barr, ; Baum et al, ; Cutler et al, ; Geronimus et al, ; Gersten, ; Institute of Medicine, ; Marmot, ; Marmot and Wilkinson, ; Mechanic, ; Shi and Stevens, ; Wilkinson, ). There is evidence, not only of racial/ethnic and SEP differentials in health measures overall but also of earlier health decline and onset of aging processes, specifically for non‐Hispanic blacks compared to non‐Hispanic whites (Geronimus et al, ; Geronimus et al, ), for those in poverty compared to those not in poverty (Crimmins et al, ), and for an interaction by race/ethnicity and SEP (measured by educational level) (Howard and Sparks, ). Geronimus and colleagues proposed the “weathering” hypothesis, which explains these differences as resulting from the biological chronic stress responses associated with cumulative exposure of minority and low SEP individuals to social, economic, and political inequality and marginalization (Geronimus, ; Geronimus et al, ; Geronimus et al, ).…”