The Impact of Social Determinants of Health on Hospitalization in the Veterans Health Administration

Cross-sectional analysis of place-based and racial disparities in hospitalisation rates by disease category in California in 2001 and 2011

“…These findings are consistent with prior work, which found that young and middle-aged residents of poor US urban neighbourhoods were at higher risk of early mortality due to chronic diseases 17 18. A recent study linking Veterans Health Administration data with US Census data showed higher hospitalisation rates in white Veterans and in Veterans livings in low-income census tracts 19. Another study mapped hospital days and zip code income for California urban areas, emphasising the importance of disaggregating county-level data by showing a strong association between low zip code income and higher percentage of disability and greater use of hospitals 20.…”

A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers

“…New CEHRT functionalities and data types captured in EHRs by PCPs are increasingly used to risk stratify and manage patient populations on a health system or community level [44] , [45] , [46] , [47] . However, the value of such CEHRT functionalities, data types (e.g., social determinants of health) [48] , [49] , [50] and potential data challenges in improving population health quality measures requires additional research [51] , [52] . The methodology used in this study can inform such studies in effectively controlling various population-level moderators and mediators while measuring the net effect of EHR features on population-level quality measures [53] .…”

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

“…Although there is a strong and compelling body of literature on the observed associations between SBDH and health, to date, diagnosis-based forecasting models used to predict cost and utilization have not yet shown the incremental value of adding SBDH risk factors to predictions. Some published reports using community-level SBDH data contribute only slightly to the predictive model performance beyond individual patient characteristics extracted from EHR data [ 43 , 44 ].…”

“…Rather than commercial data, academic centers and government organizations have primarily relied on individual-level clinical information derived from structured and unstructured EHRs [ 51 ] and relevant risk factors on a community level extracted from public surveys [ 52 ], such as the United States Census Bureau American Community Survey, which includes multiple indicators of neighborhood deprivation [ 43 , 53 ]; the Food Access Research Atlas, which describes food deserts [ 54 , 55 ]; and the American Housing Survey, which contains information on housing characteristics [ 56 , 57 ]. In one systematic review of predictive models using EHR data, 36 of the 106 unique studies included SBDH data in one of their final predictive models [ 58 ].…”