摘要：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. Efforts to prevent people from becoming homeless have increased dramatically since 2009, when the federal government distributed $1.5 billion for the Homeless Prevention and Rapid Rehousing Program as part of the American Recovery and Reinvestment Act. 1 The National Alliance to End Homelessness credits this spending with reducing homelessness by 1% between 2009 and 2011, 2 when the economic downturn might otherwise have led to its burgeoning. Even earlier, 24 of 25 cities surveyed by the US Conference of Mayors 3 had programs to prevent homelessness among families facing eviction. However, evidence that particular prevention efforts reduce homelessness remains sparse. Burt et al. 4 suggested that prevention strategies must be both effective (i.e., they must stop people from becoming homeless) and efficient (i.e., they must target help to people who would become homeless without it). Efficiency—getting services to the right people—may be the harder problem. Analysis of the American Community Survey by the Joint Center for Housing Studies has shown that 20.2 million households (18% of all US households) were severely cost burdened in 2010, paying more than half of their income for housing. This figure increased by 6.4 million households in the decade from 2001 to 2010. 5 (p27) The number and percentage of households who doubled up (shared a housing unit with another household) increased over the course of the recession, with 21.8 million households, or 18.3%, doubling up in 2011. 6 Yet despite widespread risk, most households avoid entering shelter. In this article, we develop a method for predicting which family households are most likely to become homeless in the absence of preventive services. Practitioners frequently confound good services with bad targeting, deeming prevention successful if participants do not later come to shelter 3 ; by this criterion, services could be made to appear even more effective by giving them only to millionaires. Existing targeting models are based on the accumulated wisdom of service providers but often lack empirical foundation. Many cities use a 1-factor model: eviction. 3 Hennepin County, Minnesota, whose more sophisticated targeting model for preventing family homelessness has been widely copied, recently returned to the drawing board after finding that families given prevention services differed sharply from families who became homeless. For example, 40% of prevention recipients, but 94% of homeless families, had incomes less than $1000 per month; 1% versus 33% had a household head aged younger than 22 years. 7 Ensuring that families who receive prevention services resemble families in shelter is also insufficient. For example, many families in shelter are headed by single mothers, both because single-parent families tend to be poor and because shelters in many jurisdictions exclude men. Among poor families, however, those who are single are no more likely to become homeless 8–10 or have repeat episodes. 11,12 Prior case-control studies have examined predictors of shelter entry for particular groups, 10,13–17 but only a few researchers 9,18,19 have examined the adequacy of the resulting models. In other fields, a large literature spanning more than 50 years has suggested that actuarial predictions based on statistical models are more accurate than professional or clinical judgments. 20,21 More recent reviews have confirmed the superiority of mechanical models for prediction in the majority of cases across medicine, mental health, personality, and education 22,23 but have suggested somewhat more variability. In particular, logically rather than empirically derived rules are not necessarily better than clinical judgment, 23 which in the domain of homelessness means that rules for distributing service on the basis of judgments and experience, rather than empirical models, may not be better than caseworker judgments in individual cases. With this study, we helped New York City develop an empirical targeting model to enhance the efficiency of its HomeBase prevention program for families. The program, which was shown to be effective in experimental and quasi-experimental evaluations, 24,25 provides customized services including case management, eviction prevention, landlord mediation, short-term emergency funding, and assistance in obtaining employment and public benefits to families at imminent risk of homelessness. We evaluated the relationship of risk factors collected from applicants to later shelter entry, whether some families are too risky to be helped (so that a triage model might work better than one that gives services to families at highest risk), and whether some risk factors are particularly amenable to services. We developed a screening instrument the city has begun to use to target services and evaluated the efficiency of different models.
