Dan V Blalock1,2, Matthew L Maciejewski1,3,4, Donna M Zulman5,6, Valerie A Smith1,3,4, Janet Grubber1, Ann-Marie Rosland7,8, Hollis J Weidenbacher1, Liberty Greene5,6, Leah L Zullig1,3, Heather E Whitson9,10,11, Susan N Hastings1,3,9,10,11, Anna Hung1,3,12. 1. Durham Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT), Durham Veterans Affairs Health Care System. 2. Departments of Psychiatry and Behavioral Sciences. 3. Population Health Sciences. 4. Department of Medicine, Division of General Internal Medicine, Duke University, Durham, NC. 5. Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA. 6. Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA. 7. VA Pittsburgh Center for Health Equity Research and Promotion. 8. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh PA. 9. Geriatric Research, Education, and Clinical Center, Durham Veterans Affairs Health Care System. 10. Center for the Study of Human Aging and Development, Duke University. 11. Department of Medicine, Duke University School of Medicine. 12. Duke Clinical Research Institute, Duke University, Durham, NC.
Abstract
OBJECTIVE: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.
OBJECTIVE: Population segmentation has been recognized as a foundational step to help tailor interventions. Prior studies have predominantly identified subgroups based on diagnoses. In this study, we identify clinically coherent subgroups using social determinants of health (SDH) measures collected from Veterans at high risk of hospitalization or death. STUDY DESIGN AND SETTING: SDH measures were obtained for 4684 Veterans at high risk of hospitalization through mail survey. Eleven self-report measures known to impact hospitalization and amenable to intervention were chosen a priori by the study team to identify subgroups through latent class analysis. Associations between subgroups and demographic and comorbidity characteristics were calculated through multinomial logistic regression. Odds of 180-day hospitalization were compared across subgroups through logistic regression. RESULTS: Five subgroups of high-risk patients emerged-those with: minimal SDH vulnerabilities (8% hospitalized), poor/fair health with few SDH vulnerabilities (12% hospitalized), social isolation (10% hospitalized), multiple SDH vulnerabilities (12% hospitalized), and multiple SDH vulnerabilities without food or medication insecurity (10% hospitalized). In logistic regression, the "multiple SDH vulnerabilities" subgroup had greater odds of 180-day hospitalization than did the "minimal SDH vulnerabilities" reference subgroup (odds ratio: 1.53, 95% confidence interval: 1.09-2.14). CONCLUSION: Self-reported SDH measures can identify meaningful subgroups that may be used to offer tailored interventions to reduce their risk of hospitalization and other adverse events.
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