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Published in: Journal of General Internal Medicine 4/2017

01-04-2017 | Editorial

Advancing the Understanding of Social Determinants of Health Through Geospatial Analysis

Authors: Rebekah J. Walker, PhD, Brian Neelon, PhD, Leonard E. Egede, MD, MS

Published in: Journal of General Internal Medicine | Issue 4/2017

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Excerpt

Social 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
Literature
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go back to reference Nelson K, Schwartz G, Hernandez S, Simonetti J, Curtis I, Fihn SD. The association of neighborhood environment and mortality: results from a national study of Veterans. JGIM. 2016. doi:10.1007/s11606-016-3905-x. Nelson K, Schwartz G, Hernandez S, Simonetti J, Curtis I, Fihn SD. The association of neighborhood environment and mortality: results from a national study of Veterans. JGIM. 2016. doi:10.​1007/​s11606-016-3905-x.
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go back to reference Neelon B. Spatial data analysis for health services research. In Lawson A, Banerjee S, Haining R, Ugarte L, eds. Handbook of Spatial Epidemiology, Chapter 29. Boca Raton: CRC Press; 2016: 549–562. Neelon B. Spatial data analysis for health services research. In Lawson A, Banerjee S, Haining R, Ugarte L, eds. Handbook of Spatial Epidemiology, Chapter 29. Boca Raton: CRC Press; 2016: 549–562.
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go back to reference Gelfand AE, Kim H, Sirmans CF, Banerjee S. Spatial modeling with spatially varying coefficient processes. J Am Stat Assoc. 2003;98(462):387–96.CrossRef Gelfand AE, Kim H, Sirmans CF, Banerjee S. Spatial modeling with spatially varying coefficient processes. J Am Stat Assoc. 2003;98(462):387–96.CrossRef
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go back to reference Neelon B, Ghosh P, Loebs PF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J R Stat Soc Ser A. 2013;176:389–413.CrossRef Neelon B, Ghosh P, Loebs PF. A spatial Poisson hurdle model for exploring geographic variation in emergency department visits. J R Stat Soc Ser A. 2013;176:389–413.CrossRef
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go back to reference Neelon B, Chang HC, Ling Q, Hastings SN. Spatiotemporal hurdle models for zero-inflated count data: exploring trends in emergency department visits. Stat Methods Med Res. 2014; 1–19. Neelon B, Chang HC, Ling Q, Hastings SN. Spatiotemporal hurdle models for zero-inflated count data: exploring trends in emergency department visits. Stat Methods Med Res. 2014; 1–19.
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go back to reference Neelon B, Li F, Burgette LF, Benjamin Neelon SE. A spatiotemporal quantile regression model for emergency department expenditures. Stat Med. 2015;34:2559–75.CrossRefPubMed Neelon B, Li F, Burgette LF, Benjamin Neelon SE. A spatiotemporal quantile regression model for emergency department expenditures. Stat Med. 2015;34:2559–75.CrossRefPubMed
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go back to reference Neelon B, Zhu L, Benjamin Neelon SE. Bayesian two-part spatial models for semicontinuous data with application to emergency department expenditures. Biostatistics. 2015;16:465–79.CrossRefPubMed Neelon B, Zhu L, Benjamin Neelon SE. Bayesian two-part spatial models for semicontinuous data with application to emergency department expenditures. Biostatistics. 2015;16:465–79.CrossRefPubMed
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go back to reference Diggle JP. Statistical Analysis of Spatial Point Patterns. 3rd ed. Boca Raton: CRC Press; 2013. Diggle JP. Statistical Analysis of Spatial Point Patterns. 3rd ed. Boca Raton: CRC Press; 2013.
Metadata
Title
Advancing the Understanding of Social Determinants of Health Through Geospatial Analysis
Authors
Rebekah J. Walker, PhD
Brian Neelon, PhD
Leonard E. Egede, MD, MS
Publication date
01-04-2017
Publisher
Springer US
Published in
Journal of General Internal Medicine / Issue 4/2017
Print ISSN: 0884-8734
Electronic ISSN: 1525-1497
DOI
https://doi.org/10.1007/s11606-016-3942-5

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