The Association Between Neighborhood Environment and Mortality: Results from a National Study of Veterans

Nelson, Karin M.; Schwartz, Greg; Hernandez, Susan E.; Curtis, Idamay; Fihn, Stephan D.

doi:10.1007/s11606-016-3905-x

Cited by 37 publications

(34 citation statements)

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“…23 Socioeconomic status in patients' residence areas was captured using a composite Census tract level measure. 24 We calculated standardized mean differences (SMDs) for each variable, and considered SMDs with a magnitude of greater than 0.1 as meaningful, consistent with prior studies. 25,26 We conducted two additional analyses to characterize latent subgroups identified by the k-means algorithm.…”

Section: Characterizing Latent Subgroupsmentioning

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Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

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Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

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“…2 The Veterans Health Administration (VHA) as the largest integrated health system in the nation is committed to interventions that target key SDOH factors im-pacting the health of veterans and their communities. [13][14][15] To assist the VHA to achieve their goals in patient care and population health, we assessed whether geographically derived neighborhood socioeconomic status (NSES) and local housing stock had an impact on hospitalization rates of veterans receiving care at the VHA's primary care medical home clinics.…”

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The Association Between Neighborhood Socioeconomic and Housing Characteristics with Hospitalization: Results of a National Study of Veterans

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Background: Social determinants of health (SDOH) have an inextricable impact on health. If remained unaddressed, poor SDOH can contribute to increased health care utilization and costs. We aimed to determine if geographically derived neighborhood level SDOH had an impact on hospitalization rates of patients receiving care at the Veterans Health Administration's (VHA) primary care clinics. Methods: In a 1-year observational cohort of veterans enrolled in VHA's primary care medical home program during 2015, we abstracted data on individual veterans (age, sex, race, Gagne comorbidity score) from the VHA Corporate Data Warehouse and linked those data to data on neighborhood socioeconomic status (NSES) and housing characteristics from the US Census Bureau on census tract level. We used generalized estimating equation modeling and spatial-based analysis to assess the potential impact of patient-level demographic and clinical factors, NSES, and local housing stock (ie, housing instability, home vacancy rate, percentage of houses with no plumbing, and percentage of houses with no heating) on hospitalization. We defined hospitalization as an overnight stay in a VHA hospital only and reported the risk of hospitalization for veterans enrolled in the VHA's primary care medical home clinics, both across the nation and within 1 specific case study region of the country: King County, WA. Results: Nationally, 6.63% of our veteran population was hospitalized within the VHA system. After accounting for patient-level characteristics, veterans residing in census tracts with a higher NSES index had decreased odds of hospitalization. After controlling all other factors, veterans residing in census tracts with higher percentage of houses without heating had 9% (Odds Ratio, 1.09%; 95% CI, 1.04 to 1.14) increase in the likelihood of hospitalization in our regional Washington State analysis, though not our national level analyses. Conclusions: Our results present the impact of neighborhood characteristics such as NSES and lack of proper heating system on the likelihood of hospitalization. The application of placed-based data at the geographic level is a powerful tool for identification of patients at high risk of health care utilization. (

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“…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.…”

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“…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.…”

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confidence: 99%

“…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.…”

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Advancing the Understanding of Social Determinants of Health Through Geospatial Analysis

2016

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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...

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The Association Between Neighborhood Environment and Mortality: Results from a National Study of Veterans

Cited by 37 publications

References 42 publications

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

The Association Between Neighborhood Socioeconomic and Housing Characteristics with Hospitalization: Results of a National Study of Veterans

Advancing the Understanding of Social Determinants of Health Through Geospatial Analysis

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