Predicting Daily Sheltering Arrangements among Youth Experiencing Homelessness Using Diary Measurements Collected by Ecological Momentary Assessment

Predictors of Housing Trajectories Among Young Adults Experiencing Homelessness in Los Angeles

Experiencing homelessness during young adulthood is associated with negative health outcomes and understanding housing trajectories of young adults experiencing homelessness may aid in the development of evidence-based public health programs designed to serve this at-risk age group. In the present study, the authors examined baseline predictors of 24-month trajectories of housing stability and unsheltered housing among a sample of 271 young adults aged 18 to 25 recruited from drop-in centers in Los Angeles. In multivariate models, the authors found that identifying as multi-racial/other and better friendship quality at baseline were associated with less steep increases in the likelihood of stable housing over time. Being employed at baseline was associated with a less steep decrease in the probability of being unsheltered over time, while illicit drug use days associated with a steeper decrease in the probability of being unsheltered over time. Continued research is needed to establish important factors determining young adults' long-term housing trajectories in the effort to promote greater access and engagement with housing services.

Couch-Surfing and Mental Health Outcomes among Sexual Minority Adolescents

Background

Sexual minority youth are at more than twice the risk of experiencing homelessness than their peers and both sexual minority youth and youth experiencing homelessness have disproportionate risk for mental health disorder symptoms. Couch-surfing is a common form of homelessness experienced by youth, but research on the relationship between couch-surfing and mental health outcomes, especially among sexual minority adolescents (SMA), is limited.

Methods

Utilizing a sample of 2,558 SMA (14-17 years old) recruited via social media and respondent-driven sampling, this study explores the relationship between different forms of homelessness (exclusive couch-surfing vs. multiple types of homelessness) and symptoms of depression, anxiety, suicidal ideation, and suicide attempt.

Results

Nearly 21% of participants experienced any homelessness in their lifetime, with 14% reporting exclusive couch-surfing. All forms of homelessness were associated with large increases in symptoms of anxiety, depression, suicidal ideation and suicide attempt.

Conclusion

Homelessness - primarily couch-surfing - is a common experience for SMA in this sample. All forms of homelessness - including exclusive couch-surfing - were associated with large increases in depression, anxiety, suicidal ideation, and suicide attempt, emphasizing the importance of services that are available to couch-surfing young people and responsive to the needs of sexual minority adolescents.

Understanding Homelessness among Migrants to Thunder Bay using Machine Learning (Preprint)

BACKGROUND

Over the past years, homelessness has become a substantial issue around the globe. The largest social services organization in Thunder Bay, Ontario, Canada, has observed that a majority of the people experiencing homelessness in the city were from outside of the city or province. Thus, to improve programming and resource allocation for people experiencing homelessness in the city, including shelter use, it was important to investigate the trends associated with homelessness and migration.

OBJECTIVE

This study aimed to address 3 research questions related to homelessness and migration in Thunder Bay: What factors predict whether a person who migrated to the city and is experiencing homelessness stays or leaves shelters? If an individual stays, how long are they likely to stay? What factors predict stay duration?

METHODS

We collected the required data from 2 sources: a survey conducted with people experiencing homelessness at 3 homeless shelters in Thunder Bay and the database of a homeless information management system. The records of 110 migrants were used for the analysis. Two feature selection techniques were used to address the first and third research questions, and 8 machine learning models were used to address the second research question. In addition, data augmentation was performed to improve the size of the data set and to resolve the class imbalance problem. The area under the receiver operating characteristic curve value and cross-validation accuracy were used to measure the models' performances while avoiding possible model overfitting.

RESULTS

Factors predicting an individual's stay duration included home or previous district, highest educational qualification, recent receipt of mental health support, migrating to visit family or friends, and finding employment upon arrival. For research question 2, among the classification models developed for predicting the stay duration of migrants, the random forest and gradient boosting tree models presented better results with area under the receiver operating characteristic curve values of 0.91 and 0.93, respectively. Finally, home district, band membership, status card, previous district, and recent support for drug and/or alcohol use were recognized as the factors predicting stay duration.

CONCLUSIONS

Applying machine learning enables researchers to make predictions related to migrants' homelessness and investigate how various factors become determinants of the predictions. We hope that the findings of this study will aid future policy making and resource allocation to better serve people experiencing homelessness. However, further improvements in the data set size and interpretation of the identified factors in decision-making are required.

Who Is Couch-Surfing and Who Is on the Streets? Disparities Among Racial and Sexual Minority Youth in Experiences of Homelessness

PURPOSE

Youth experiencing homelessness (YEH) often engage in "couch-surfing," or frequently moving from one tenuous living arrangement to another. Understanding the characteristics and risk factors associated with couch-surfing is necessary to designing adequate responses to youth homelessness. The present study aims to investigate factors associated with youth at risk of couch-surfing or sleeping on the streets relative to staying in a shelter.

METHODS

The present study used Homeless Management Information System administrative data sourced from 16 communities across the U.S. between January 2015 and February 2017 (n = 9,417). Multinomial logistic regression models were used to explore correlates (e.g., demographics, homeless histories, risk and victimization, behavioral health, and self-sufficiency) of couch-surfing or sleeping on the streets relative to staying in an emergency shelter program.

RESULTS

YEH identifying as female; Black or another non-Latinx youth of color; or as lesbian, gay, bisexual, questioning, or another sexual orientation (LGBQ+) were at greater risk of couch-surfing relative to staying in a shelter. YEH who threatened to harm themselves or others in the preceding 12 months or who attributed their homelessness to an abusive relationship were significantly more likely to either be couch-surfing or on the streets.

DISCUSSION

Service providers must recognize and validate the vulnerabilities and risks experienced by couch-surfing YEH in order to help reduce barriers to accessing services faced by this population. Federal definitions of homelessness should be aligned to correct systemic biases and more accurately reflect the realities of how youth experience homelessness.

Unsheltered homeless and unstably housed adults have higher levels of stress and more health risk factors than sheltered homeless adults

In the United States, approximately 580,000 individuals were homeless on a single night in 2020. Homelessness can be categorized into three subgroups: sheltered homeless, unsheltered homeless, and unstably housed. Few studies have examined the relations between homelessness subtypes, shelter service utilization, levels of stress experienced, and health risk factors. This study aimed to empirically examine whether shelter status the previous night was related to current stress, recent utilization of shelter-based mental health services, and current health risk factors. Data were collected at multiple homeless shelters in 2016 in the Oklahoma City area (N=575). All participants completed assessments of demographic characteristics, including age, sex, race, marital status, years of education, and incarceration history and victimization. Multiple linear and logistic regression analyses were conducted to examine relations between homelessness subgroups and outcomes (shelter-based service utilization, health risk factors, and stressors). Results indicated that the sheltered group was younger and more likely to be White than the unsheltered group, had higher levels of education, and reported more lifetime months in jail than the unstably housed group. In addition, unsheltered homeless and unstably housed adults used fewer shelter-based health services, exhibited more health risk factors, experienced greater levels of stress, and had higher levels of food insecurity than sheltered homeless adults. Homeless adults who reside at shelters benefit most from available shelter services. The development of policies and programs targeted toward increasing sheltering options for unsheltered and unstably housed adults is needed.