NSES was negatively associated with total and major AL subindices in a nationally representative sample of U.S. adults—effects independent of individual-level characteristics, such as race/ethnicity, gender, and household IPR. Indeed, strong findings for the summary AL index, and weaker but consistently negative associations for individual biologic parameters, provide compelling evidence NSES impacts health status through its simultaneous and cumulative impact on multiple interrelated biologic systems. Use of a cumulative index allowed us to better capture the sum total of the more modest differences in some of the biomarkers that contribute to significant differences in overall biological risks by NSES.[37
Additionally, our findings indicate consistent effects across race/ethnic, gender, and IPR subgroups, suggesting health benefits of living in a higher SES neighbourhood accrue to individuals regardless of these individual characteristics, and indicate that, on average, members of these major population groups do not differ in their vulnerability to the greater biological wear and tear associated with living in lower SES neighbourhoods. However, a new generation of epigenetic studies may further shed light on how adverse life conditions impact gene expression, which, in turn, may be manifest in measures such as AL.
Our findings have important population health implications. In considering the health impact of NSES, both the size of NSES effects on AL and the substantial variation in NSES nationally are important. For example, disparities in health are illuminated by differences in neighbourhoods within the metropolitan areas of Washington, DC and Detroit, Michigan. In the three-mile drive from Washington’s Capitol Hill neighbourhood to the nearby Anacostia neighbourhood, NSES decreases by 65 percent. Our findings indicate such a change in NSES is associated with an average of 0.12 point higher (worse) AL and is even starker when we consider individuals living in the two Detroit communities divided by Alter Road. Travelling from Grosse Point to the adjoining East Detroit, NSES drops 86 percent. Our findings indicate this would entail a 0.26 point difference in AL between the individuals living in these two communities.
] provides further relevance for these AL differences, finding that, on average, a one-unit higher AL is associated with a 17 percent increase in mortality in the NHANES III cohort. Applying these results to our findings, we predict a 2 percent increase in mortality for people living in Anacostia versus those in the Capitol Hill neighbourhood and a 4 percent increase for individuals living in East Detroit versus Grosse Point. Moreover, the relationship between AL and mortality is stronger in younger adults[38
]; in these two examples, adults 25–35 would experience increased mortality of 7 percent and 16 percent, respectively. These examples do not represent the full range of NSES in the nation. Thus, comparing well-known, severely disadvantaged, and highly affluent neighbourhoods would represent a larger difference in NSES and in estimated mortality risk. We also expect health trajectories and outcomes might differ by NSES, just as they do by individual-level SES. If so, these illustrative examples may be conservative because they assume health trajectories do not differ by NSES beyond the AL impact. Finally, such estimates likely understate the full impact of differences in NSES on AL or mortality, because they reflect “point-in-time” estimates; actual neighbourhood effects are likely cumulative over the life course, whether their NSES has changed or varied over time.
Others have suggested NSES “gets under the skin” and contributes to health disparities through social networks and social support, health behaviours, and hypervigilance in response to neighbourhood safety concerns or other stressors, such as unemployment and discrimination.[4
] Also, built environment characteristics highly correlated to NSES are thought to play a role, including residential crowding, walkability, and access to high-quality food. Thus, the environments we live in can enhance or constrain the opportunity to pursue a healthy life, thereby contributing to socioeconomic disparities in health and mortality.[18
] Furthermore, as one reviewer noted, we also need research on individual biologic pathways through which NSES affects health.
The study has several strengths, including the use of a nationally representative sample of U.S. adults and biological data and the ability to control for multiple individual-level socioeconomic and demographic characteristics, thus allowing us to assess NSES’s independent contribution. However, this study has some important limitations. First, using cross-sectional data means we cannot determine whether the NSES and AL relationship is causal. For example, we cannot control for neighbourhood self-selection (i.e., those in poor health end up in poor neighbourhoods). However, using cumulative biological risk measures, rather than overt disease, may help minimize the potential effects of such self-selection, if they exist. Unlike overt disease, which may spur some individuals to make major life changes, individuals are far less likely to be aware of, much less to have made decisions about, whether (and where) to move based on their AL. Moreover, the relationship between NSES and AL held even when individuals with overt conditions (including cancer, stroke, and heart disease) were excluded. This secondary analysis reduces the possibility our findings reflect movement of individuals with health conditions into poorer neighbourhoods. However, NSES and AL may also have reciprocal effects over time or across generations. We need longitudinal studies to address these questions.
Second, operationalising AL using NHANES measures also leads to some potential limitations. For example, NHANES’s inflammation measures are limited. Moreover, by excluding CRP levels in calculating AL for individuals with a current infection, we may have excluded some with chronically high inflammation levels, thereby leading to underestimating the relationship between NSES and inflammation.[50
Third, research on “neighbourhoods” is limited by the need to conceptualize and operationalise geographic spaces. It is difficult to apply geographic boundaries nationally that are meaningful on an individual and programmatic or policy level.[6
] While somewhat imprecise as measures of neighbourhood context, census-tract characteristics have been used in most neighbourhood studies.[14
] However, the resulting measurement error when applied to the broader construct of “neighbourhood” suggests our findings are likely conservative. This possibility is increased because excluded subjects were more likely to have lower educational attainment and family income and live in poorer neighbourhoods. Also, because addresses that could not be geocoded were primarily from rural residents, our results may not be generalisable to more rural populations.
Finally, NHANES III data allow us to assess the relationship between NSES and AL nationally from 1988 to 1994 and to estimate its impact on mortality based on cohort survival. The NCHS is now geocoding subsequent NHANES data, but to our knowledge, NHANES III data are the only national population data that include AL measures and for which geocoding has already been completed. Examining additional geocoded NHANES data will allow us to assess whether the relationship between NSES and AL has changed over time, independent of individual-level characteristics.
Despite these limitations, our findings have important policy implications. They demonstrate that beyond individual-level socioeconomic factors, where one lives is independently associated with AL, thereby suggesting policies that improve NSES may also yield health returns. Williams et al.[4
] argue that the disproportionate distribution of Blacks and Mexican-Americans in low-NSES neighbourhoods is largely due to segregation. Hence, rather than limiting the discussion about policies that can address disparities to those focusing on individual-level health improvements, our findings suggest improving neighbourhood socioeconomic conditions may have significant long-term impacts on improving health and reducing health disparities.