Elham Hatef1, Kelly M Searle2, Zachary Predmore2, Elyse C Lasser2, Hadi Kharrazi2, Karin Nelson3, Philip Sylling4, Idamay Curtis4, Stephan D Fihn3, Jonathan P Weiner2. 1. Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. Electronic address: ehatef1@jhu.edu. 2. Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 3. Veterans Affairs Puget Sound Health Care System, Seattle, Washington; Department of Medicine, University of Washington, Seattle, Washington. 4. Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
Abstract
INTRODUCTION: This study aims to assess the effect of individual and geographic-level social determinants of health on risk of hospitalization in the Veterans Health Administration primary care clinics known as the Patient Aligned Care Team. METHODS: For a population of Veterans enrolled in the primary care clinics, the study team extracted patient-level characteristics and healthcare utilization records from 2015 Veterans Health Administration electronic health record data. They also collected census data on social determinants of health factors for all U.S. census tracts. They used generalized estimating equation modeling and a spatial-based GIS analysis to assess the role of key social determinants of health on hospitalization. Data analysis was completed in 2018. RESULTS: A total of 6.63% of the Veterans Health Administration population was hospitalized during 2015. Most of the hospitalized patients were male (93.40%) and white (68.80%); the mean age was 64.5 years. In the generalized estimating equation model, white Veterans had a 15% decreased odds of hospitalization compared with non-white Veterans. After controlling for patient-level characteristics, Veterans residing in census tracts with the higher neighborhood SES index experienced decreased odds of hospitalization. A spatial-based analysis presented variations in the hospitalization rate across the Veterans Health Administration primary care clinics and identified the clinic sites with an elevated risk of hospitalization (hotspots) compared with other clinics across the country. CONCLUSIONS: By linking patient and population-level data at a geographic level, social determinants of health assessments can help with designing population health interventions and identifying features leading to potentially unnecessary hospitalization in selected geographic areas that appear to be outliers.
INTRODUCTION: This study aims to assess the effect of individual and geographic-level social determinants of health on risk of hospitalization in the Veterans Health Administration primary care clinics known as the Patient Aligned Care Team. METHODS: For a population of Veterans enrolled in the primary care clinics, the study team extracted patient-level characteristics and healthcare utilization records from 2015 Veterans Health Administration electronic health record data. They also collected census data on social determinants of health factors for all U.S. census tracts. They used generalized estimating equation modeling and a spatial-based GIS analysis to assess the role of key social determinants of health on hospitalization. Data analysis was completed in 2018. RESULTS: A total of 6.63% of the Veterans Health Administration population was hospitalized during 2015. Most of the hospitalized patients were male (93.40%) and white (68.80%); the mean age was 64.5 years. In the generalized estimating equation model, white Veterans had a 15% decreased odds of hospitalization compared with non-white Veterans. After controlling for patient-level characteristics, Veterans residing in census tracts with the higher neighborhood SES index experienced decreased odds of hospitalization. A spatial-based analysis presented variations in the hospitalization rate across the Veterans Health Administration primary care clinics and identified the clinic sites with an elevated risk of hospitalization (hotspots) compared with other clinics across the country. CONCLUSIONS: By linking patient and population-level data at a geographic level, social determinants of health assessments can help with designing population health interventions and identifying features leading to potentially unnecessary hospitalization in selected geographic areas that appear to be outliers.
Authors: Charlie M Wray; Marzieh Vali; Louise C Walter; Lee Christensen; Samir Abdelrahman; Wendy Chapman; Salomeh Keyhani Journal: Fed Pract Date: 2021-01
Authors: Elham Hatef; Zachary Predmore; Elyse C Lasser; Hadi Kharrazi; Karin Nelson; Idamay Curtis; Stephan Fihn; Jonathan P Weiner Journal: AIMS Public Health Date: 2019-07-03
Authors: Ruth M Reeves; Lee Christensen; Jeremiah R Brown; Michael Conway; Maxwell Levis; Glenn T Gobbel; Rashmee U Shah; Christine Goodrich; Iben Ricket; Freneka Minter; Andrew Bohm; Bruce E Bray; Michael E Matheny; Wendy Chapman Journal: J Biomed Inform Date: 2021-06-24 Impact factor: 8.000
Authors: Donna M Zulman; Matthew L Maciejewski; Janet M Grubber; Hollis J Weidenbacher; Dan V Blalock; Leah L Zullig; Liberty Greene; Heather E Whitson; Susan N Hastings; Valerie A Smith Journal: JAMA Netw Open Date: 2020-10-01