Youth homelessness is a significant problem in most United States communities. Health problems are both a contributor to and a consequence of homelessness. Responses to youth homelessness are typically fragmentary. Different agencies deal with various causes and consequences of the problem. Stakeholders in Connecticut sought a more coherent approach. This article describes the development and use of a system dynamics simulation model as a decision-support tool that: (1) brings stakeholders together from diverse service sectors and allows them to see the system as a whole, (2) enables them to explore how delivery systems interact to affect homeless and unstably housed youth, (3) lets them test the impact of different intervention alternatives on reducing the problem, and (4) helps develop insights about coherent approaches to youth homelessness. The model’s development is described as a phased process including stakeholder engagement, causal mapping, and creation of the quantitative simulation model. The resulting model is presented along with an interface that enables stakeholders to use the model in a Learning Lab setting. Results of an initial set of Learning Labs are presented, including types of insights gained by participants from using the simulation model. Conclusions include limitations of the model and plans for its future use.

Little is known about the health, mental health, and sexual risks experienced by a more hidden population of homeless youth who are solely “couch surfing” (temporarily staying with others) and largely disconnected from services. This paper describes the housing characteristics and risk patterns of this understudied population of homeless youth who are solely couch-surfing and compares their risk patterns to literally homeless youth. A hybrid form of respondent-driven sampling (network-based sampling) was used to recruit 75 homeless young people aged 14-24. A convergent parallel mixed-methods design was used to collect, analyze and interpret the qualitative (interview) and quantitative (survey) data. Quantitative survey data were collected on social support, housing and risk characteristics (mental health, substance use, and sexual risk behaviors). A subset (n=30) of survey respondents participated in a follow-up in-depth interview to further contextualize the risks associated with couch surfing. Non-parametric and parametric methods were performed on survey data to compare characteristics between couch-surfing only and literally homeless youth. A thematic analysis was used to identify common themes in interview transcripts. The qualitative and quantitative data were then compared for convergence and divergence. Results suggest more similarities than differences in risk between couch surfing only and literally homeless youth. No significant differences were found in housing and risk characteristics. Perceived social support was the only significant difference between couch-surfing and literally homeless youth. These findings demonstrate an urgent need for policy reform and specialized resources for couch surfing youth to prevent any further harm among this vulnerable population.