In:
Medical Care, Ovid Technologies (Wolters Kluwer Health), Vol. 59, No. 5 ( 2021-05), p. 410-417
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
0025-7079
DOI:
10.1097/MLR.0000000000001526
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2021
detail.hit.zdb_id:
2045939-7
