Advancing the Understanding of Social Determinants of Health Through Geospatial Analysis

Abstract

S ocial determinants of health, such as neighborhood characteristics and residential segregation, are increasingly recognized as factors significantly influencing health outcomes and contributing to inequities in health. 1,2 As health care systems are asked to address the social and environmental factors influencing their patients, a better understanding of the relationship between different social determinants of health and outcomes is necessary. In this issue of JGIM, Nelson et al. examine the association between neighborhood socioeconomic status (NSES) and all-cause mortality in a national sample of veterans. 3 Using Veteran Health Administration (VHA) data on individual veteran characteristics and US Census Bureau data on census tract characterized NSES, Nelson et al. found those in the lowest (poorest) decile were 10% more likely to die than those living in the highest (wealthiest) decile, after adjustment for socio-demographics, individual income, and comorbidities. 3 The authors should be commended for their analysis incorporating available spatial information and a national sample of veterans, addressing limitations of prior studies by using a population with racial/ethnic and income diversity and simultaneously adjusting for individual comorbidities and socioeconomic factors. 3 Results from Nelson et al.'s study provide further evidence for inclusion of social determinants of health in decisions influencing provision of high-quality health care, such as risk adjustment and resource allocation. Additionally, it serves as an example of the increasing role spatial data analysis is playing in health services research and in understanding the role of social determinants. A wide variety of spatial analysis models and analytic frameworks are available to provide information about community needs and barriers to care and to provide guidance to policy makers as they consider how to allocate resources in the most cost-effective manner. 4 Although Nelson et al. obtain global estimates of the association between NSES and mortality while accounting for within-county correlation, the spatial random effect is used chiefly as a mechanism to account for this association. A natural extension of this work, expanding further into the field of spatial analysis, would be to examine spatial patterns in mortality across the study region after establishing a global effect of NSES. In fact, it might be of particular interest to explore whether the effect of NSES on mortality itself varies across space, through the inclusion of spatially varying regression coefficients for NSES, allowing investigators to identify areas where NSES has the most profound impact on mortality. 5 We anticipate that spatial statistical methods will play an increasingly important role in the design and implementation of community-based efforts to improve access to care while reducing health costs. As seen in the paper by Nelson et al., the VA is well suited to conduct work using spatial analysis. As the largest managed care provider in the US, the VA has...

Connected Community Classification (C3): Development, Validation, and Geospatial Application for Population Health Promotion and Equity

“…Selecting ZCTA to define community and as the analytical unit increases the clinical or public application of the findings through better alignment to individual understanding of place-based community residence 49. ZCTAs are reliable, have broader applicability, are highly correlated to other regional boundaries, and better represent diversity of rural communities 26–29…”

“…ZCTAs are larger and more diverse than census tracts or block groups, providing the ability to more reliability estimate SDOH influence in more rural geography and across communities 26,27. ZCTA boundaries are highly correlated with census tracts and block groups and can be applied to investigating social and health phenomena in geospatial networks 28,29…”

“…26,27 ZCTA boundaries are highly correlated with census tracts and block groups and can be applied to investigating social and health phenomena in geospatial networks. 28,29…”

“…49 ZCTAs are reliable, have broader applicability, are highly correlated to other regional boundaries, and better represent diversity of rural communities. [26][27][28][29] Despite the evident strengths of selecting ZCTA as the determination of a community boundary, this decision for unifying analysis could result in potential misclassification or bias and may not accurately represent all communities. Population perceptions of community may vary from physical boundaries of place and represent networks of shared identity, beliefs, and social norms, which we were not able to capture presently.…”