arXiv subjects

Janelle Rothfolk

Publications and source records attributed to Janelle Rothfolk.

3 recordsLinked to original sources

arXiv2026

A Quasi-Experiment comparing the health of unhoused people who have and have not experienced an eviction in King County, WA

Home eviction poses a significant threat to housing stability, a critical determinant of health. This study examines the relationship between eviction and health and substance use within the unhoused population of King County, Washington. Using a sample of 1,106 individuals experiencing homelessness, we employed a quasi-experimental design to compare the health outcomes of those who have experienced eviction with those who have not. Our findings reveal eviction is associated with an 8.3% point increase (SE = 0.039) in the likelihood of reporting poor general health and an 9.5% increase (SE = 0.032) in substance use disorder. No significant effect was found for mental health outcomes. While these results highlight the severe health risks linked to eviction, further research with more precise estimates is necessary to better understand long-term effects. These findings contribute to the growing evidence of how home eviction undermines the well-being of vulnerable populations.

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arXiv2024

Understanding the Personal Networks of People Experiencing Homelessness in King County, WA with aggregate Relational Data

The social networks of people experiencing homelessness are an understudied but vital aspect of their lives, offering access to information, support, and safety. In 2023, the U.S. Department of Housing and Urban Development reported 653,100 people experiencing homelessness on any given night -- a 23% rise since 2022, though likely an undercount. This paper examines a unique three-year dataset (2022-2024) of survey responses from over 3,000 unhoused individuals in King County, WA, collected via network-based sampling methods to estimate the unsheltered population. Our study analyzes the networks of the unsheltered population, focusing on acquaintance, close friendship, kinship, and peer referral networks. Findings reveal a decline in social connectivity over time. The average number of acquaintances dropped from 80 in 2023 to 40 in 2024. Close friendship levels remained stable at 2.5, but given the growth in the homeless population, this suggests decreased network connectivity. Kinship networks expanded, indicating that more family members of unhoused individuals are also experiencing homelessness. These trends suggest increasing social disconnection, possibly driven by displacement and a rise in newly homeless individuals. The growing isolation may reduce opportunities for information sharing and mutual support. However, the increased reliance on family networks highlights the shifting dynamics of social support within this community. This research underscores the need for policies fostering social connections and community building, such as reducing displacement and providing spaces for congregation, to counter the growing anomie among unhoused populations.

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arXiv2023

Network Sampling Methods for Estimating Social Networks, Population Percentages, and Totals of People Experiencing Unsheltered Homelessness

In this article, we propose using network-based sampling strategies to estimate the number of unsheltered people experiencing homelessness within a given administrative service unit, known as a Continuum of Care. We demonstrate the effectiveness of network sampling methods to solve this problem. Here, we focus on Respondent Driven Sampling (RDS), which has been shown to provide unbiased or low-biased estimates of totals and proportions for hard-to-reach populations in contexts where a sampling frame (e.g., housing addresses) is not available. To make the RDS estimator work for estimating the total number of people living unsheltered, we introduce a new method that leverages administrative data from the HUD-mandated Homeless Management Information System (HMIS). The HMIS provides high-quality counts and demographics for people experiencing homelessness who sleep in emergency shelters. We then demonstrate this method using network data collected in Nashville, TN, combined with simulation methods to illustrate the efficacy of this approach and introduce a method for performing a power analysis to find the optimal sample size in this setting. We conclude with the RDS unsheltered PIT count conducted by King County Regional Homelessness Authority in 2022 (data publicly available on the HUD website) and perform a comparative analysis between the 2022 RDS estimate of unsheltered people experiencing homelessness and an ARIMA forecast of the visual unsheltered PIT count. Finally, we discuss how this method works for estimating the unsheltered population of people experiencing homelessness and future areas of research.

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