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

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.

[1]  Beth Ann Griffin,et al.  Housing affordability and health among homeowners and renters. , 2010, American journal of preventive medicine.

[2]  J. Skinner,et al.  A population health approach to reducing observational intensity bias in health risk adjustment: cross sectional analysis of insurance claims , 2014, BMJ : British Medical Journal.

[3]  M. Whooley,et al.  Leading causes of cardiovascular hospitalization in 8.45 million US veterans , 2018, PloS one.

[4]  T. Seeman,et al.  Neighborhoods and cumulative biological risk profiles by race/ethnicity in a national sample of U.S. adults: NHANES III. , 2009, Annals of epidemiology.

[5]  Ethan A. Halm,et al.  Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review , 2013, Journal of General Internal Medicine.

[6]  Timothy W. Collins,et al.  Sexual Orientation, Gender, and Environmental Injustice: Unequal Carcinogenic Air Pollution Risks in Greater Houston , 2017, Annals of the American Association of Geographers.

[7]  K Y Liang,et al.  Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.

[8]  Sebastian Schneeweiss,et al.  A combined comorbidity score predicted mortality in elderly patients better than existing scores. , 2011, Journal of clinical epidemiology.

[9]  Bin Huang,et al.  Housing code violation density associated with emergency department and hospital use by children with asthma. , 2014, Health affairs.

[10]  D. Nerenz,et al.  Socioeconomic status and readmissions: evidence from an urban teaching hospital. , 2014, Health affairs.

[11]  Genevieve B. Melton,et al.  Residence, Living Situation, and Living Conditions Information Documentation in Clinical Practice , 2017, AMIA.

[12]  J. Hardoy,et al.  Housing and health. , 1987, BMJ.

[13]  Deborah Bachrach,et al.  Addressing Patients' Social Needs: An Emerging Business Case for Provider Investment , 2014 .

[14]  E. Bradley,et al.  Leveraging the Social Determinants of Health: What Works? , 2016, PloS one.

[15]  Pemetaan Jumlah Balita,et al.  Spatial Scan Statistic , 2014, Encyclopedia of Social Network Analysis and Mining.

[16]  Corinne M. Graffunder,et al.  The National Prevention Strategy: leveraging multiple sectors to improve population health. , 2015, American journal of public health.

[17]  Sze Yan Liu,et al.  Hospital Readmissions for Childhood Asthma: The Role of Individual and Neighborhood Factors , 2009, Public health reports.

[18]  Steinwachs Dm,et al.  Accounting for Social Risk Factors in Medicare Payment , 2017 .

[19]  Neha J. Pagidipati,et al.  Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data , 2018, JAMA network open.

[20]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.

[21]  B. Waterman,et al.  Adding socioeconomic data to hospital readmissions calculations may produce more useful results. , 2014, Health affairs.

[22]  Elizabeth S. Chen,et al.  Evaluation of Flowsheet Documentation in the Electronic Health Record for Residence, Living Situation, and Living Conditions , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[23]  Stanley Xu,et al.  Community-level correlates of hospitalizations for persons with schizophrenia. , 2009, Psychiatric services.

[24]  Steven H. Brown,et al.  VistA - U.S. Department of Veterans Affairs national-scale HIS , 2003, Int. J. Medical Informatics.

[25]  J. Haas,et al.  Housing instability and food insecurity as barriers to health care among low-income americans , 2007, Journal of General Internal Medicine.

[26]  D. Salkever,et al.  The cost-effectiveness of independent housing for the chronically mentally ill: do housing and neighborhood features matter? , 2004, Health services research.

[27]  Janelle Downing,et al.  The health effects of the foreclosure crisis and unaffordable housing: A systematic review and explanation of evidence. , 2016, Social science & medicine.

[28]  Genevieve B. Melton,et al.  Representation of Social History Factors Across Age Groups: A Topic Analysis of Free-Text Social Documentation , 2017, AMIA.

[29]  S. Fihn,et al.  The Association Between Neighborhood Environment and Mortality: Results from a National Study of Veterans , 2017, Journal of General Internal Medicine.

[30]  J. Schwartz,et al.  Vulnerability to renal, heat and respiratory hospitalizations during extreme heat among U.S. elderly , 2016, Climatic Change.

[31]  S. Burgard,et al.  Housing instability and health: findings from the Michigan Recession and Recovery Study. , 2012, Social science & medicine.