Background with rationale The US homeless population has had a fairly unique age structure for thirty years, with one-third concentrated among the 1955-1965 birth cohort. They are aging prematurely and are experiencing aging related morbidity and mortality in increasing numbers. This study uses demographic methods to project the growth in aged homelessness out to 2030. Using linked administrative data, historical patterns of health care, nursing home and shelter are used to estimate future costs. Potential offsets are estimated from anticipated reductions in excess services use associated with housing placement. Main Aim To determine if housing placement among future aged homeless adults could be offset by reduced health and social service costs. Methods/Approach Demographic methods are applied to historical shelter data in Los Angeles, New York and Boston to project the future growth in aged homelessness. Linked administrative data are used to estimate future age-related use of health and social services. Housing models are applied to meet population needs to estimate costs. Health and social service costs offsets are estimated from prior literature. The net cost is estimated based on the differential. Results Elderly homelessness will triple in the US over the next ten years. Average annual health and shelter costs will be $22-28,000 per person. Housing costs are estimated at $7-11,000 annually. Cost offsets yield a positive ROI of 1.13 in New York and Los Angeles; Boston results are closer to break-even. Conclusion Substantial public dollars will be spent on the aged homelessness problem as it triples over the next ten years. Some of those dollars could be shifted to housing solutions, reducing homelessness significantly, with little to no increase in public expenditures; positive returns on such investments are also possible.

People who are homeless use more hospital-based care than average, yet little is known about how hospital and shelter use are interrelated. We examined the timing of emergency department (ED) visits and hospitalizations relative to entry into and exit from New York City homeless shelters, using an analysis of linked health care and shelter administrative databases. In the year before shelter entry and the year following shelter exit, 39.3 percent and 43.3 percent, respectively, of firsttime adult shelter users had an ED visit or hospitalization. Hospital visits-particularly ED visits-began to increase several months before shelter entry and declined over several months after shelter exit, with spikes in ED visits and hospitalizations in the days immediately before shelter entry and following shelter exit. We recommend cross-system collaborations to better understand and address the co-occurring health and housing needs of vulnerable populations.

No 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.

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.