Kelly M Doran1,2, Eileen Johns3, Sara Zuiderveen4, Marybeth Shinn5, Kinsey Dinan6, Maryanne Schretzman3, Lillian Gelberg7,8,9, Dennis Culhane10, Donna Shelley11, Tod Mijanovich12. 1. Department of Emergency Medicine, NYU School of Medicine, New York, New York, USA. 2. Department of Population Health, NYU School of Medicine, New York, New York, USA. 3. NYC Center for Innovation through Data Intelligence, New York, New York, USA. 4. Prevention and Housing Support, Homelessness Prevention Administration, NYC Human Resources Administration, New York, New York, USA. 5. Department of Human and Organizational Development, Vanderbilt University, Nashville, Tennessee, USA. 6. Office of Research and Policy Innovation, NYC Department of Social Services, New York, New York, USA. 7. Department of Family Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA. 8. Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California, USA. 9. Office of Healthcare Transformation and Innovation, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA. 10. School of Social Policy and Practice, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 11. Public Health Policy and Management, NYU School of Global Public Health, New York, New York, USA. 12. Applied Statistics and Health Policy, Department of Applied Statistics, Social Science, and Humanities, NYU Steinhardt School, New York, New York, USA.
Abstract
OBJECTIVE: To develop a screening tool to identify emergency department (ED) patients at risk of entering a homeless shelter, which could inform targeting of interventions to prevent future homelessness episodes. DATA SOURCES: Linked data from (1) ED patient baseline questionnaires and (2) citywide administrative homeless shelter database. STUDY DESIGN: Stakeholder-informed predictive modeling utilizing ED patient questionnaires linked with prospective shelter administrative data. The outcome was shelter entry documented in administrative data within 6 months following the baseline ED visit. Exposures were responses to questions on homelessness risk factors from baseline questionnaires. DATA COLLECTION/EXTRACTION METHODS: Research assistants completed questionnaires with randomly sampled ED patients who were medically stable, not in police/prison custody, and spoke English or Spanish. Questionnaires were linked to administrative data using deterministic and probabilistic matching. PRINCIPAL FINDINGS: Of 1993 ED patients who were not homeless at baseline, 5.6% entered a shelter in the next 6 months. A screening tool consisting of two measures of past shelter use and one of past criminal justice involvement had 83.0% sensitivity and 20.4% positive predictive value for future shelter entry. CONCLUSIONS: Our study demonstrates the potential of using cross-sector data to improve hospital initiatives to address patients' social needs.
OBJECTIVE: To develop a screening tool to identify emergency department (ED) patients at risk of entering a homeless shelter, which could inform targeting of interventions to prevent future homelessness episodes. DATA SOURCES: Linked data from (1) ED patient baseline questionnaires and (2) citywide administrative homeless shelter database. STUDY DESIGN: Stakeholder-informed predictive modeling utilizing ED patient questionnaires linked with prospective shelter administrative data. The outcome was shelter entry documented in administrative data within 6 months following the baseline ED visit. Exposures were responses to questions on homelessness risk factors from baseline questionnaires. DATA COLLECTION/EXTRACTION METHODS: Research assistants completed questionnaires with randomly sampled ED patients who were medically stable, not in police/prison custody, and spoke English or Spanish. Questionnaires were linked to administrative data using deterministic and probabilistic matching. PRINCIPAL FINDINGS: Of 1993 ED patients who were not homeless at baseline, 5.6% entered a shelter in the next 6 months. A screening tool consisting of two measures of past shelter use and one of past criminal justice involvement had 83.0% sensitivity and 20.4% positive predictive value for future shelter entry. CONCLUSIONS: Our study demonstrates the potential of using cross-sector data to improve hospital initiatives to address patients' social needs.
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