Marybeth Shinn1, Andrew L Greer, Jay Bainbridge, Jonathan Kwon, Sara Zuiderveen. 1. Marybeth Shinn and Andrew L. Greer are with the Department of Human and Organizational Development, Peabody College, Vanderbilt University, Nashville, TN. Jay Bainbridge is with Marist College School of Management, Poughkeepsie, NY. Jonathan Kwon and Sara Zuiderveen are with the Department of Homeless Services, New York, NY.
Abstract
OBJECTIVES: We developed and evaluated a model to target homelessness prevention services to families more efficiently. METHODS: We followed 11,105 families who applied for community-based services to prevent homelessness in New York City from October 1, 2004, to June 30, 2008, through administrative records, using Cox regression to predict shelter entry. RESULTS: Over 3 years, 12.8% of applicants entered shelter. Both the complete Cox regression and a short screening model based on 15 risk factors derived from it were superior to worker judgments, with substantially higher hit rates at the same level of false alarms. We found no evidence that some families were too risky to be helped or that specific risk factors were particularly amenable to amelioration. CONCLUSIONS: Despite some limitations, an empirical risk model can increase the efficiency of homelessness prevention services. Serving the same proportion of applicants but selecting those at highest risk according to the model would have increased correct targeting of families entering shelter by 26% and reduced misses by almost two thirds. Parallel models could be developed elsewhere.
OBJECTIVES: We developed and evaluated a model to target homelessness prevention services to families more efficiently. METHODS: We followed 11,105 families who applied for community-based services to prevent homelessness in New York City from October 1, 2004, to June 30, 2008, through administrative records, using Cox regression to predict shelter entry. RESULTS: Over 3 years, 12.8% of applicants entered shelter. Both the complete Cox regression and a short screening model based on 15 risk factors derived from it were superior to worker judgments, with substantially higher hit rates at the same level of false alarms. We found no evidence that some families were too risky to be helped or that specific risk factors were particularly amenable to amelioration. CONCLUSIONS: Despite some limitations, an empirical risk model can increase the efficiency of homelessness prevention services. Serving the same proportion of applicants but selecting those at highest risk according to the model would have increased correct targeting of families entering shelter by 26% and reduced misses by almost two thirds. Parallel models could be developed elsewhere.
Authors: M Shinn; B C Weitzman; D Stojanovic; J R Knickman; L Jiménez; L Duchon; S James; D H Krantz Journal: Am J Public Health Date: 1998-11 Impact factor: 9.308
Authors: E L Bassuk; J C Buckner; L F Weinreb; A Browne; S S Bassuk; R Dawson; J N Perloff Journal: Am J Public Health Date: 1997-02 Impact factor: 9.308
Authors: Kelly M Doran; Eileen Johns; Sara Zuiderveen; Marybeth Shinn; Kinsey Dinan; Maryanne Schretzman; Lillian Gelberg; Dennis Culhane; Donna Shelley; Tod Mijanovich Journal: Health Serv Res Date: 2021-10-20 Impact factor: 3.402