Patient-Reported Social and Behavioral Determinants of Health and Estimated Risk of Hospitalization in High-Risk Veterans Affairs Patients

Abstract

IMPORTANCE Despite recognition of the association between individual social and behavioral determinants of health (SDH) and patient outcomes, little is known regarding the value of SDH in explaining variation in outcomes for high-risk patients. OBJECTIVE To describe SDH factors among veterans who are at high risk for hospitalization, and to determine whether adding patient-reported SDH measures to electronic health record (EHR) measures improves estimation of 90-day and 180-day all-cause hospital admission. DESIGN, SETTING, AND PARTICIPANTS A survey was mailed between April 16 and June 29, 2018, to a nationally representative sample of 10 000 Veterans Affairs (VA) patients whose 1-year risk of hospitalization or death was in the 75th percentile or higher based on a VA EHR-derived risk score. The survey included multiple SDH measures, such as resilience, social support, health literacy, smoking status, transportation barriers, and recent life stressors. MAIN OUTCOMES AND MEASURES The EHR-based characteristics of survey respondents and nonrespondents were compared using standardized differences. Estimation of 90-day and 180-day hospital admission risk was assessed for 3 logistic regression models: (1) a base model of all prespecified EHR-based covariates, (2) a restricted model of EHR-based covariates chosen via forward selection based on minimizing Akaike information criterion (AIC), and (3) a model of EHRand survey-based covariates chosen via forward selection based on AIC minimization. RESULTS In total, 4685 individuals (response rate 46.9%) responded to the survey. Respondents were comparable to nonrespondents in most characteristics, but survey respondents were older (eg, >80 years old, 881 [18.8%] vs 800 [15.1%]), comprised a higher percentage of men (4391 [93.7%] vs 4794 [90.2%]), and were composed of more White non-Hispanic individuals (3366 [71.8%] vs 3259 [61.3%]). Based on AIC, the regression model with survey-based covariates and EHR-based covariates better estimated hospital admission at 90 days (AIC, 1947.7) and 180 days (AIC, 2951.9) than restricted models with only EHR-based covariates (AIC, 1980.2 at 90 days; AIC, 2981.9 at 180 days). This result was due to inclusion of self-reported measures such as marital or partner status, health-related locus of control, resilience, smoking status, health literacy, and medication insecurity. CONCLUSIONS AND RELEVANCE Augmenting EHR data with patient-reported social information improved estimation of 90-day and 180-day hospitalization risk, highlighting specific SDH factors that might identify individuals who are at high risk for hospitalization.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…Of these studies, 13 used a retrospective cohort design, 68 86 99 100 101 102 103 104 105 106 107 108 109 1 used an observational design, 110 1 used a cross-sectional design, 111 2 adopted a prognostic approach, 30 112 2 performed a longitudinal analysis, 113 114 and 2 used survey data. 115 116 A vast majority of studies used EHR data, while the remaining eight studies used administrative databases 100 106 107 109 111 114 or surveys as the primary collection tool. 115 116 Study populations included adults 30 with chronic illnesses, 100 105 107 114 pediatrics, 86 106 veterans, 116 COVID-19 patients, 99 101 102 103 108 110 113 hospice patients, 104 inpatients, 68 112 and Medicare recipients.…”

“…115 116 A vast majority of studies used EHR data, while the remaining eight studies used administrative databases 100 106 107 109 111 114 or surveys as the primary collection tool. 115 116 Study populations included adults 30 with chronic illnesses, 100 105 107 114 pediatrics, 86 106 veterans, 116 COVID-19 patients, 99 101 102 103 108 110 113 hospice patients, 104 inpatients, 68 112 and Medicare recipients. 109 111 115 All studies were based in the United States.…”

Individual-level and neighborhood-level factors associated with longitudinal changes in cardiometabolic measures in participants of a care coordination program

“…[65, 66] Third, though burdensome, individual-level social risks, rather than neighborhood-level risks, can improve the ability to predict which patients may benefit from supplementary community-based interventions. [67-69] Lastly, we acknowledge that the LIGHT 2 care coordination program focused on super-utilizers and reducing care fragmentation rather than reducing health disparities. [27]…”

Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Care Coordination Program

“…[47,48] Third, though burdensome, individual-level social and behavior risks, rather than neighborhood-level risks, can improve the ability to predict which patients may bene t from supplementary community-based interventions. [49][50][51] Lastly, we acknowledge that the LIGHT 2 care coordination program focused on super-utilizers and reducing care fragmentation rather than reducing health disparities. [22]…”