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.

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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.

Author Contributions: KMD, MS, DC, DS, TM, EJ, MS, and SZ contributed to the study idea. KMD and DS obtained funding for the study. KMD led survey data collection with assistance from RG and IW. EJ and MS of NYC CIDI performed administrative data linkage. TM and KMD conducted data analyses. KMD drafted the manuscript and all authors contributed to its revision and approved the final version. KMD takes responsibility for the work as a whole.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.