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

Abstract

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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“…Hatef et al 21 furthers the evidence indicating social determinants create health differences. By linking Veterans Health Administration medical record data with neighborhood socioeconomic status, a complex picture emerges showing the association between housing factors (such as percentage of houses without plumbing and heating) to an increased likelihood of hospitalization.…”

Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims

“…Health care providers are increasingly using EHR data, along with insurance claims, to improve the management of their patient populations. [22][23][24][25][26][27][28][29] Medication complexity and adherence indices can boost such efforts by providing clinicians and case managers with key information to improve treatment outcomes and reduce utilization. 11 In this study, we reviewed medication data from a 2-year retrospective cohort of 70,054 patients with linked claims and EHR data provided by an integrated health care delivery network.…”

“…Health systems operating within value-based boundaries (e.g., accountable care organizations) are increasingly using all types of clinical data, including EHRs and claims, for population health management purposes. [22][23][24][25][26][27][28][29] However, only a few studies have investigated EHR-derived MRCI against claims-derived MPR, and none have offered a population-level scope. [30][31][32] Furthermore, no attempt has been made to compare all 3 indices of MRCI, MPR, and PFR in a patient population of a health system.…”

Assessing the geographical distribution of comorbidity among commercially insured individuals in South Africa

“…Data generated in this study has significant potential to contribute towards greater public health efforts at a national, provincial, district and even electoral ward level especially if linked to national data sources such as Census data. By incorporating in risk stratification analyses social factors known to play a significant role in the health of individuals throughout life and therefore the overall health of populations, a deeper understanding of the financial impact of local determinants of health may be possible [29].…”