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. 2025 May 22;2024:1303–1312.

Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence

Rachael Zehrung 1,*, Di Hu 1,*, Yawen Guo 1, Kai Zheng 1, Yunan Chen 1
PMCID: PMC12099419  PMID: 40417547

Abstract

Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children’s overall health and development. Drawing on data from the 2022 National Survey of Children’s Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.

Introduction

As pandemic-era protections subside and rental prices increase, housing instability in the United States has intensified, with eviction filings and cost-burdened households on the rise.1,2 As a pervasive and multifaceted challenge, housing instability encompasses issues related to the affordability, safety, quality, and stability of housing.3 This challenge manifests in several forms, such as overcrowding, moving frequently, missing rent or mortgage payments, and being evicted.4 Experiencing eviction and unstable living is known to negatively affect children’s well-being, increasing their risk of developmental delays, behavioral issues, and mental health conditions.5–8 Such experiences also have long-term impacts on their life outcomes; for example, childhood housing instability is associated with a higher risk of depression and anxiety in adulthood.9 Moreover, housing instability disproportionately burdens youth with marginalized identities (e.g., BIPOC and LGBTQ+), who are already vulnerable to mental health challenges due to other compounding social inequalities (e.g., discrimination).10

Prior work has found that housing instability amplifies both physical and mental health disparities, demonstrating its role as a critical social determinant of health.11,12 Social determinants of health (SDoH), defined as “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life,”13 include factors such as economic stability, education access and quality, neighborhood and built environment, social and community context, and healthcare access and quality.14 A growing body of research explores the complex interplay among SDoH, emphasizing the influence of upstream social determinants (e.g., socioeconomic resources) on downstream social determinants (e.g., health behaviors).15,16 For instance, housing instability is linked to postponed medical care and increased emergency department visits for children in low-income families.17 These findings underscore the importance and necessity of understanding SDoH to address health inequity and improve child health outcomes.

Families facing housing instability may also encounter barriers such as lacking a primary care provider, health insurance, and transportation tools, which can impede children from accessing necessary health care.18,19 Despite the evident effects of housing instability on children’s mental health, little work has examined how housing instability influences children’s usage of mental health services. This study bridges this gap by examining how various SDoH, especially housing instability, affect children’s mental health outcomes and their access to mental health care. In light of the challenges of accessing care faced by youth experiencing housing instability, technology-based mental health interventions show promise, though existing work tends to focus on older adolescents and young adults.20,21 Building on the risk and protective factors identified in our study for children experiencing housing instability, we outline the potential opportunities for digital mental health interventions to support the delivery of mental health services to this population.

With the goal of identifying opportunities for digital mental health interventions, this study investigates the following research questions:

  1. How do housing instability and other sociodemographic factors influence the occurrence of anxiety and depression among youth aged 6-17 years old?

  2. How does housing instability affect mental health treatment usage among youth aged 6-17 years old with mental health conditions?

We hypothesized that housing instability increases the likelihood of mental health conditions in children while significantly hindering their access to mental health treatment.

Methods

This study first investigated a range of social and demographic factors that influence youth mental health and then examined the effect of housing instability on youth with mental health conditions. In this section, we describe our data source, selection of independent and dependent variables, and data analysis approach.

Data Source. The data used in this study come from the 2022 National Survey of Children’s Health (NSCH), which is a nationally representative survey on the physical and emotional health of children (aged 0-17) in the United States. The survey is directed by the Health Resources and Service Administration’s Maternal and Child Health Bureau (HRSA MCHB) and conducted by the U.S. Census Bureau on an annual basis. The NSCH uses a national sample of addresses and administers a screening questionnaire to identify households with children, followed by an age-based topical questionnaire for eligible households. The 2022 NSCH used a sample of 360,000 addresses with a weighted overall response rate of 39.1%. Of the 122,000 completed screener questionnaires, 67,269 households were eligible for a topiccal questionnaire follow-up, which 54,103 households completed. Our analysis considered children aged 617, resulting in a dataset with 34,362 households before data cleaning. More details on the survey methodology can be found in the U.S. Census Bureau’s methodology report.22

Dependent Variables. The occurrence of anxiety/depression and use of mental health treatment were defined as dependent variables (Table 1). We categorized a child as having anxiety or depression only if the respondent indicated the child currently has the condition because we aimed to examine relationships between children’s current living situations and mental health. We re-coded the question asking about mental health treatment reception (K2Q22_R) to be binary (i.e., “yes” or “no” to receiving treatment).

Table 1.

Survey questions used to assess the occurrence of depression/ anxiety and use of mental health treatment

Q_ID Question Options
K2Q32A Has a doctor or other health care provider EVER told you that this child has Depression?

  1. Yes

  2. No
K2Q32B If yes, does this child CURRENTLY have the condition?

  1. Yes

  2. No
K2Q33A Has a doctor or other health care provider EVER told you that this child has Anxiety Problems?

  1. Yes

  2. No
K2Q33B If yes, does this child CURRENTLY have the condition?

  1. Yes

  2. No
K4Q22_R DURING THE PAST 12 MONTHS, has this child received any treatment or counseling from a mental health professional?

  1. Yes

  2. No, but this child needed to see a mental health professional

  3. No, this child did not need to see a mental health professional
Mental health professionals include psychiatrists, psychologists, psychiatric nurses, and clinical social workers.
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Independent Variables. Informed by prior work on housing instability and the SDoH framework, we selected social and demographic factors to understand their associations with youth mental health. For demographic information, we considered factors such as children’s age, gender, race, body mass index (BMI), and family structure. Independent variables in our final analyses were selected based on theoretical relevance to our research objectives and a multicollinearity test (Variance Inflation Factor).

Housing Instability: Based on definitions of housing instability,23,24 we used a combination of three survey questions to assess families’ housing situations (Table 2). Given that the threat of eviction and eviction itself are significant indicators of housing instability,25 we identified respondents as housing unstable if they were always, usually, or sometimes worried about eviction (HOMEEVIC). In line with prior research on housing instability, we combined multiple moves (PLACESLIVED) and being behind on rent or mortgage payments (MISSMORTGAGE) in the past year as one indicator.26 Prior work has found that the number of moves is not sensitive enough as a single-item indicator to determine housing instability, as moving could be voluntary and positive.23 Similarly, missing one rent or mortgage payment might not reflect longitudinal patterns of housing instability.27 An exploratory analysis confirmed that the combination of PLACESLIVED and MISSMORTGAGE was a more reliable indicator of housing instability than either one individually.

Table 2.

Survey questions used to assess housing instability

Q_ID Question Options
HOMEEVIC DURING THE PAST 12 MONTHS, how often were you worried or stressed about being evicted, foreclosed on, or having your housing condemned?

  1. Always

  2. Usually

  3. Sometimes

  4. Rarely

  5. Never
MISSMORTGAGE DURING THE PAST 12 MONTHS, was there a time when you were not able to pay the mortgage or rent on time?

  1. Yes

  2. No

  3. Don’t Know
PLACESLIVED DURING THE PAST 12 MONTHS, how many places has this child lived?

  1. 0-2 places

  2. 3 or more places
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Neighborhood Environment: As SDoH, the neighborhoods that people live in and the interactions that they have within their communities have an impact on their overall health and well-being.14,28 Social support, for example, can buffer the negative effects imposed by other risks to health.29 We included questions that asked respondents about their subjective views on their community (e.g., social support) and the presence of certain features in their neighborhoods (e.g., sidewalks and libraries). By including these factors, we aimed to develop a more complete understanding of respondents’ living situations and which factors contribute to children’s mental health.

Social Context: Children’s participation in extracurricular activities is associated with positive psychological and behavioral outcomes, such as higher self-esteem and lower rates of depression.30,31 Extracurricular activities can be particularly beneficial for marginalized youth, who tend to participate less but experience greater benefits.32,33 Recognizing the impact of extracurricular activities on children’s health, we included relevant survey questions about extracurricular participation to understand their effect on the mental health of children experiencing housing instability (Table 4).

Table 4.

Survey questions used to account for children’s participation in extracurricular activities

Q_ID Question: DURING THE PAST 12 MONTHS, did this child participate in: Options
K7Q30 A sports team or did they take sports lessons after school or on weekends?

  1. Yes

  2. No
K7Q32 Any other organized activities or lessons, such as music, dance, language, or other arts?

  1. Yes

  2. No
K7Q37 Any type of community service or volunteer work at school, place of worship, or in the community?

  1. Yes

  2. No
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Data Analysis. NSCH survey data were analyzed using R software (version 4.3.3). Prior to conducting any statistical analyses, we performed data cleaning and preprocessing which included re-coding certain questions to define our dependent and independent variables (as described above) and excluding cases with missing values. Data from 30,364 households ended up being included in our final analysis. To explore the influence of sociodemographic factors on the occurrence of anxiety and depression (RQ1), we employed multiple logistic regression models for each condition. We hypothesized that sociodemographic factors play significant roles in explaining variations in mental health outcomes among youth aged 6-17 years. To examine how housing instability affects mental health treatment usage, particularly among youth with mental health conditions (RQ2), we first constructed a multiple logistic regression model with 12-month mental health treatment usage as the dependent variable, controlling for all other sociodemographic factors. We then introduced interaction terms between the occurrence of mental health conditions (anxiety and depression, respectively) and housing instability to specifically examine the impact of housing instability on mental health treatment usage among youth with mental conditions. This focus is driven by our interest in the subgroup most affected by these factors, rather than in youth without mental health issues. We hypothesize that youth with mental health conditions, although in need, are less likely to receive mental health treatment services if they are experiencing housing instability.

Results

Descriptive Statistics. In the final analytical sample of 30,364 children, 51.9% were male. In terms of race, 77.3% were White, 7.5% were Black or African American, and the remaining 16.3% were identified as Other. The mean age of children was 12 (Standard Deviation: 3.5). Within this population, 7.1% were identified as experiencing housing instability. Furthermore, 16.4% and 7.3% were reported as currently experiencing anxiety and depression, respectively. In the 12 months prior to the survey, 17.9% had received professional mental health treatment. Based on this sample, we conducted regression analysis to answer our research questions.

RQ1: How do housing instability and other sociodemographic factors influence the occurrence of anxiety and depression among youth aged 6-17 years old? In this section, we present the results from the logistic regression models (Table 5) that used the occurrence of anxiety and depression as dependent variables.

Table 5.

Model results with “current_anxiety” and “current_depression” as dependent variables.

Current_Anxiety Current_Depression
Variable Odds.Ratio P-value Odds.Ratio P-value
95% CI 95% CI
Sex (Female) 1.57 ***
(1.47 - 1.68)		 2.03 ***
(1.84 - 2.24)
Age 1.12 ***
(1.11 - 1.13)		 1.29 ***
(1.27 - 1.31)
Race (White) Reference
Race (Black) 0.33 ***
(0.28 - 0.39)		 0.36 ***
(0.28 - 0.44)
Race (Other) 0.57 ***
(0.52 - 0.63)		 0.74 ***
(0.64 - 0.84)
BMI (μ5th percentile) Reference
BMI (5-85th percentile) 0.94 (0.83 - 1.06) 0.93 (0.77 - 1.14)
BMI (85-95th percentile) 1.03 (0.89 - 1.18) 1.18 (0.95 - 1.47)
BMI (>=95th percentile) 1.15 *
(1 - 1.32)		 1.58 ***
(1.28 - 1.96)
Family_structure
(Two biological/adoptive parents, married) Reference
Family_structure
(Two biological/adoptive parents, non-married) 0.91 (0.75 - 1.1) 1.05 (0.78 - 1.39)
Family_structure
(Two parents, married, at least one not biological/adoptive) 1.42 ***
(1.26 - 1.61)		 1.87 ***
(1.58 - 2.2)
Family_structure
(Two parents, non-married, at least one not biological/adoptive) 1.52 ***
(1.23 - 1.88)		 2.61 ***
(1.99 - 3.4)
Family_structure (Single mother) 1.39 ***
(1.27 - 1.51)		 1.82 ***
(1.61 - 2.05)
Family_structure (Single father) 0.70 ***
(0.6 - 0.82)		 0.94 (0.74 - 1.17)
Family_structure (Grandparent household) 1.68 ***
(1.41 - 2)		 2.88 ***
(2.3 - 3.59)
Family_structure (Other relation) 1.89 ***
(1.43 - 2.49)		 3.67 ***
(2.66 - 5.02)
Sports Team or Lessons (No) 1.58 ***
(1.48 - 1.69)		 1.86 ***
(1.68 - 2.05)
Other Organized Activities or Lessons (No) 0.95 (0.89 - 1.02) 1.13 *
(1.02 - 1.25)
Community Service or Volunteer Work (No) 1.23 ***
(1.14 - 1.32)		 1.29 ***
(1.16 - 1.43)
Neighborhood_Vandalism (No) 0.81 ***
(0.72 - 0.91)		 0.81 **
(0.68 - 0.95)
Neighborhood_Recreation Center (No) 1.03 (0.97 - 1.1) 1.18 ***
(1.07 - 1.3)
Safe_at_School (Definitely Agree) Reference
Safe_at_School (Somewhat Agree) 1.51 ***
(1.41 - 1.61)		 1.70 ***
(1.54 - 1.87)
Safe_at_School (Somewhat Disagree) 2.36 ***
(2.03 - 2.74)		 3.09 ***
(2.56 - 3.72)
Safe_at_School (Definitely Disagree) 4.24 ***
(3.34 - 5.38)		 5.61 ***
(4.24 - 7.39)
Housing_Instability (Yes) 1.42 ***
(1.27 - 1.59)		 1.57 ***
(1.36 - 1.82)
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p-value <0.001 ***, <0.01 **, <0.05 *

Demographic Factors: We found that sex, age, race, and family structure were significant predictors of anxiety and depression. Female children were twice as likely to have depression (p<0.001) and 57% more likely to have anxiety (p<0.001) compared to male children. As a child’s age increased by one year, their likelihood of experiencing anxiety and depression increased by 12% and 29% (p<0.001), respectively. Our analysis showed that Black children and other children of color were significantly less likely to have depression and anxiety.

Housing Instability: We found that housing instability had a significant effect on the occurrence of anxiety and depression. Controlling for other sociodemographic factors, children experiencing housing instability were more likely to have anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Driven by our particular interest in the housing unstable population, we conducted a follow-up subgroup analysis after reviewing these results from the general models. Comparing children in stable housing and children in unstable housing, we found that social and demographic factors affected children at similar rates regardless of housing status, with the exception of family structure. Children who lived with relatives other than parents or grandparents in unstable housing were significantly more likely to have depression (OR: 14.23, p<0.001) and anxiety (OR: 4.20, p<0.001) than children who lived with other relatives in stable housing.

Social Factors: Children in neighborhoods with vandalism were 19% more likely to have anxiety (p<0.001) and depression (p<0.01). Likewise, children in neighborhoods without a recreation center were 18% more likely to have depression (p<0.001), though there was no effect on anxiety. As parental perceptions of children’s safety at school decreased, the likelihood of children having anxiety and depression increased. For example, when parents felt that their children were definitely not safe at school, children were 4.24 times more likely to have anxiety (p<0.001) and 5.61 times more likely to have depression (p<0.001). Taken together, these results suggest that neighborhood and community characteristics have a significant effect on children’s mental health, particularly when those characteristics relate to perceptions of safety. Further, we found that children who did not participate in sports were more likely to have anxiety (OR: 1.58, p<0.001) and depression (OR: 1.86, p<0.001). Similarly, children who did not participate in community service or volunteer work were more likely to have anxiety (OR: 1.23, p<0.001) and depression (OR: 1.29, p<0.001). Participation in other organized activities or lessons had no significant effect on anxiety and a smaller effect on depression (OR: 1.13, p<0.05).

RQ2: How does housing instability affect mental health treatment usage among youth aged 6-17 years old with mental health conditions? Despite the increased likelihood of mental health conditions among the housing unstable population, children with depression and housing instability were 38% less likely to use mental health services (p<0.01) compared to children who did not have depression and lived in stable housing. Similarly, children with anxiety and housing instability were 39% less likely to use mental health services (p<0.001). The role of housing instability is further evident in that children with depression were 22 times more likely (p<0.001) to receive mental health treatment if they had stable housing. Likewise, children with anxiety were 17 times more likely (p<0.001) to receive treatment if they had stable housing.

Discussion

This study investigated the effects of housing instability and other SDoH on children’s mental health outcomes and access to mental health treatment. Echoing prior work, our analysis revealed disparities in mental health outcomes based on demographic and social factors. Specifically, we found that children experiencing housing instability were significantly more likely to have anxiety and depression, yet they were much less likely to receive mental health treatment compared to children in stable housing. Families experiencing housing instability encounter financial, structural, and cognitive barriers to healthcare access and service utilization.34 These barriers include limited health insurance, lack of a primary care provider, and difficulty navigating the healthcare system, which disrupt the continuity of care for children. To overcome these barriers to care and address mental health disparities among marginalized populations, prior work has highlighted the potential for digital mental health (DMH) solutions due to their accessibility, scalability, and cost effectiveness.35 Digital mental health technologies include web-based platforms, mobile applications, wearable devices, and other tools used to monitor health, share health-related information, and deliver care. Although youth cannot change their housing situations or actively seek professional treatment on their own, DMH technologies can be used to bridge the gap in treatment access by providing youth with free or low-cost services that they can access from anywhere. Despite extensive research on DMH for both children and individuals with unstable housing, little work has examined the intersection of these populations. Our work contributes to the growing call to consider housing stability as a fundamental aspect of children’s health. Based on our findings, we discuss considerations for DMH interventions that target youth experiencing housing instability.

Considering Children of Different Ages: In designing DMH technologies for children, it is imperative to consider the role of both demographic factors (e.g., age) and technical factors (e.g., device ownership). Existing DMH interventions for youth tend to focus on adolescents and young adults, while little work engages with younger children under the age of 10.36 However, symptoms of anxiety and depression can onset early in childhood,37,38 and our results confirmed that children face an increased mental health risk as they age. That said, delivering DMH interventions to younger children presents challenges. While smartphone ownership rates are high among adolescents regardless of socioeconomic background, only a minority of children under the age of 11 have their own smartphones.39,40 Due to low rates of device ownership among younger children, we highlight the potential of collaborative DMH interventions that involve caregivers in scaffolding children’s social emotional learning.41 Prior work has found that DMH interventions for youth tend to be more effective when they involve regular interactions with a therapist, peer, or parent, in comparison to fully automated or self-administered interventions.42 Given that youth experiencing housing instability might have less access to therapists or parents (e.g., if they live with other relatives), future research should explore the role of other caregivers in facilitating DMH. Though DMH technologies cannot replace professional treatment, they can bridge the gap for children with less access to mental health services. For youth with their own devices, DMH interventions can be individual or collaborative.

Considering Social Connection and Context: Research suggests that youth value social connection when using digital health technologies, particularly with peers experiencing similar challenges.43 In particular, adolescents perceive peer communication and support as essential aids in managing their mental health.44 For youth experiencing housing instability, higher levels of social connectedness have been associated with resilience and improved mental health.45 This perspective aligns with our findings, which demonstrated the positive effect of social involvement (e.g., sports activities and community service) on children’s mental health. While children experiencing housing instability face barriers to participating in group and community activities in-person, often as a result of frequent moves, the online connectedness facilitated by DMH technologies may offer them opportunities to tackle this issue.46 Furthermore, DMH technologies should consider the social context of youth experiencing housing instability; for example, living with relatives other than parents or grandparents might reduce children’s sense of privacy and increase their reluctance to openly use DMH tools in front of or with help from others. Future work should explore the needs and preferences of youth experiencing housing instability by actively engaging them in the co-design process to ensure the acceptability of the final design.

Limitations: Our analysis showed that BIPOC youth exhibited lower rates of depression and anxiety; however, these findings might not accurately represent the mental health landscape due to racial disparities within the survey population, as over 75% of the population was White. Further, BIPOC youth may be underdiagnosed (or misdiagnosed) and undertreated for depression and anxiety.47 Our model for predicting mental health service in the past 12 months supports this claim by verifying that Black children and children of color were less likely to have received any professional mental health service. Our analysis was further limited by the questions asked in the survey. For example, we were unable to evaluate reasons for why children did not receive mental health treatment. Future work might consider integrating quantitative and qualitative approaches to gain more insight into the challenges faced by families experiencing housing instability as they navigate their children’s mental health. We also emphasize that while DMH interventions often focus on individual behaviors, it is crucial for policymakers to address upstream SDoH (e.g., housing policies) to combat fundamental causes of health inequalities.

Conclusion

Our study underscores the significant impact of housing instability on children’s mental health outcomes and access to mental health services. We found that youth facing housing instability are at a heightened risk of experiencing anxiety and depression, highlighting the need for targeted interventions to address these disparities. Furthermore, our findings revealed significant gaps in mental health treatment access for children experiencing both housing instability and mental health conditions. This study contributes to an understanding of the complex interplay between SDoH and children’s mental health, emphasizing the importance of addressing housing instability as a public health concern. Based on our findings, we discussed the design of age-appropriate and socially supportive DMH interventions tailored to the unique needs and challenges of children experiencing housing instability.

Table 3.

Survey questions used to define neighborhood characteristics

Q_ID Question Options
K10Q13 In your neighborhood, is/are there:
A recreation center, community center, or boys’ and girls’ club?

  1. Yes

  2. No
K10Q23 In your neighborhood, is/are there:
Vandalism such as broken windows or graffiti?

  1. Yes

  2. No
K10Q14 To what extent do you agree with these statements about your neighborhood or community?

  1. Definitely agree

  2. Somewhat agree

  3. Somewhat disagree

  4. Definitely disagree
This child is safe at school
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Table 6.

Model results with “Mental_Health_Treatment” as the dependent variable, controlling all other sociodemographic factors in the previous models.

Table 6.1,

adding an interaction term between “Current_Anxiety” and “Housing_Instability.”

Odds.Ratio P-value
Interaction Terms 95% CI
Current_Anxiety (Yes) 17.4 ***
(16.03 - 18.79)
Housing_Instability (Yes) 1.01 (0.85 - 1.2)
Current_Anxiety (Yes) : Housing_Instability (Yes) 0.61 ***
(0.47 - 0.78)
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p-value <0.001 ***, <0.01 **, <0.05 *

Table 6.2,

adding an interaction term between “Current_Depression” and “Housing_Instability.”

Interaction Terms Odds.Ratio P-value
95% CI
Current_Depression (Yes) 22.33 ***
(19.73 - 25.33)
Housing_Instability (Yes) 0.91 (0.78 - 1.05)
Current Depression (Yes) : Housing Instability (Yes) 0.62 **
(0.46 - 0.85)
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p-value <0.001 ***, <0.01 **, <0.05 *

References

  • 1.Casey M, Rico RJ. Eviction filings are 50% higher than they were pre-pandemic in some cities as rents rise [Internet] AP News. 2023. [cited 2024 Mar 12]. Available from: https://apnews.com/article/evictions-homelessness-affordable-housing-landlords-rental-assistance-dc4a03864011334538f82d2f404d2afb.
  • 2.Whitney P. More Than 42 Million US Households Were Cost Burdened in 2022 | Joint Center for Housing Studies [Internet] 2024. [cited 2024 Mar 12]. Available from: https://www.jchs.harvard.edu/blog/more-42-million-us-households-were-cost-burdened-2022.
  • 3.Tait K, Carter GR. The History of Housing Insecurity Research at PD&R | HUD USER [Internet] 2023. [cited 2024 Mar 13]. Available from: https://www.huduser.gov/portal/pdredge/pdr-edge-pdrat50-103123.html.
  • 4.Frederick TJ, Chwalek M, Hughes J, Karabanow J, Kidd S. How Stable Is Stable? Defining and Measuring Housing Stability. J Community Psychol. 2014;42(8):964–79. [Google Scholar]
  • 5.Haskett ME, Armstrong JM, Tisdale J. Developmental Status and Social–Emotional Functioning of Young Children Experiencing Homelessness. Early Child Educ J. 2016 Mar 1;44(2):119–25. [Google Scholar]
  • 6.Cutts DB, Meyers AF, Black MM, Casey PH, Chilton M, Cook JT, et al. US Housing Insecurity and the Health of Very Young Children. Am J Public Health. 2011 Aug;101(8):1508–14. doi: 10.2105/AJPH.2011.300139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Fowler PJ, Henry DB, Marcal KE. Family and housing instability: Longitudinal impact on adolescent emotional and behavioral well-being. Soc Sci Res. 2015 Sep 1;53:364–74. doi: 10.1016/j.ssresearch.2015.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Beharry MS, Christensen R. Homelessness in Pediatric Populations: Strategies for Prevention, Assistance, and Advocacy. Pediatr Clin. 2020 Apr 1;67(2):357–72. doi: 10.1016/j.pcl.2019.12.007. [DOI] [PubMed] [Google Scholar]
  • 9.Keen R, Chen JT, Slopen N, Sandel M, Copeland WE, Tiemeier H. Prospective Associations of Childhood Housing Insecurity With Anxiety and Depression Symptoms During Childhood and Adulthood. JAMA Pediatr. 2023 Aug 1;177(8):818–26. doi: 10.1001/jamapediatrics.2023.1733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.McKinnon II. Experiences of Unstable Housing Among High School Students — Youth Risk Behavior Survey, United States, 2021. MMWR Suppl [Internet] 2023. [cited 2023 Nov 20];72. Available from: https://www.cdc.gov/mmwr/volumes/72/su/su7201a4.htm. [DOI] [PMC free article] [PubMed]
  • 11.Glick JL, Lopez A, Pollock M, Theall KP. Housing insecurity and intersecting social determinants of health among transgender people in the USA: A targeted ethnography. Int J Transgender Health. 2020 Jul 2;21(3):337–49. doi: 10.1080/26895269.2020.1780661. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Hill-Briggs F, Adler NE, Berkowitz SA, Chin MH, Gary-Webb TL, Navas-Acien A, et al. Social Determinants of Health and Diabetes: A Scientific Review. Diabetes Care. 2021 Jan;44(1):258–79. doi: 10.2337/dci20-0053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.World Health Organization. Social determinants of health [Internet] [cited 2024 Mar 12]. Available from: https://www.who.int/health-topics/social-determinants-of-health.
  • 14.U.S. Department of Health and Human Services. Social Determinants of Health - Healthy People 2030 | health.gov [Internet] [cited 2024 Mar 10]. Available from: https://health.gov/healthypeople/priority-areas/social-determinants-health.
  • 15.Braveman P, Egerter S, Williams DR. The Social Determinants of Health: Coming of Age. Annu Rev Public Health. 2011;32(1):381–98. doi: 10.1146/annurev-publhealth-031210-101218. [DOI] [PubMed] [Google Scholar]
  • 16.Alegría M, NeMoyer A, Falgàs Bagué I, Wang Y, Alvarez K. Social Determinants of Mental Health: Where We Are and Where We Need to Go. Curr Psychiatry Rep. 2018 Sep 17;20(11):95. doi: 10.1007/s11920-018-0969-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ma CT, Gee L, Kushel MB. Associations Between Housing Instability and Food Insecurity With Health Care Access in Low-Income Children. Ambul Pediatr. 2008 Jan 1;8(1):50–7. doi: 10.1016/j.ambp.2007.08.004. [DOI] [PubMed] [Google Scholar]
  • 18.Wood D, Valdez RB. Barriers to Medical Care for Homeless Families Compared With Housed Poor Families. Am J Dis Child. 1991 Oct 1;145(10):1109–15. doi: 10.1001/archpedi.1991.02160100041021. [DOI] [PubMed] [Google Scholar]
  • 19.Lewis JH, Andersen RM, Gelberg L. Health Care for Homeless Women. J Gen Intern Med. 2003;18(11):921–8. doi: 10.1046/j.1525-1497.2003.20909.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Schueller SM, Glover AC, Rufa AK, Dowdle CL, Gross GD, Karnik NS, et al. A Mobile Phone–Based Intervention to Improve Mental Health Among Homeless Young Adults: Pilot Feasibility Trial. JMIR MHealth UHealth. 2019 Jul 2;7(7):e12347. doi: 10.2196/12347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Lal S, Elias S, Sieu V, Peredo R. The Use of Technology to Provide Mental Health Services to Youth Experiencing Homelessness: Scoping Review. J Med Internet Res. 2023 Jan 16;25(1):e41939. doi: 10.2196/41939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.U.S. Census Bureau. 2022 National Survey of Children’s Health Methodology Report [Internet] 2024 Feb. [cited 2024 Mar 10]. Available from: https://www2.census.gov/programs-surveys/nsch/technical-documentation/methodology/2022-NSCH-Methodology-Report.pdf.
  • 23.Farero A, Sullivan CM, López-Zerón G, Bowles RP, Sprecher M, Chiaramonte D, et al. Development and validation of the housing instability scale. J Soc Distress Homelessness. 2022;0(0):1–10. doi: 10.1080/10530789.2022.2127852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Routhier G. Beyond Worst Case Needs: Measuring the Breadth and Severity of Housing Insecurity Among Urban Renters. Hous Policy Debate. 2019 Mar 4;29(2):235–49. [Google Scholar]
  • 25.DeLuca S, Rosen E. Housing Insecurity Among the Poor Today. Annu Rev Sociol. 2022;48(1):343–71. [Google Scholar]
  • 26.De Marchis EH, Ettinger de Cuba SA, Chang L, Sheward RS, Doran KM, Gottlieb LM, et al. Screening Discordance and Characteristics of Patients With Housing-Related Social Risks. Am J Prev Med. 2021 Jul 1;61(1):e1–12. doi: 10.1016/j.amepre.2021.01.027. [DOI] [PubMed] [Google Scholar]
  • 27.Kim H, Burgard SA, Seefeldt KS. Housing Assistance and Housing Insecurity: A Study of Renters in Southeastern Michigan in the Wake of the Great Recession. Soc Serv Rev. 2017 Mar;91(1):41–70. doi: 10.1086/690681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Singh GK, Siahpush M, Kogan MD. Neighborhood Socioeconomic Conditions, Built Environments, And Childhood Obesity. Health Aff (Millwood) 2010 Mar;29(3):503–12. doi: 10.1377/hlthaff.2009.0730. [DOI] [PubMed] [Google Scholar]
  • 29.Singh R, Javed Z, Yahya T, Valero-Elizondo J, Acquah I, Hyder AA, et al. Community and Social Context: An Important Social Determinant of Cardiovascular Disease. Methodist DeBakey Cardiovasc J. 17(4):15–27. doi: 10.14797/mdcvj.846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Fredricks JA, Eccles JS. Is extracurricular participation associated with beneficial outcomes? Concurrent and longitudinal relations. Dev Psychol. 2006 Jul;42(4):698–713. doi: 10.1037/0012-1649.42.4.698. [DOI] [PubMed] [Google Scholar]
  • 31.Metsäpelto RL, Pulkkinen L. The benefits of extracurricular activities for socioemotional behavior and school achievement in middle childhood: An overview of the research. J Educ Res Online. 2014;6(3):10–33. [Google Scholar]
  • 32.Heath RD, Anderson C, Turner AC, Payne CM. Extracurricular Activities and Disadvantaged Youth: A Complicated—But Promising—Story. Urban Educ. 2022 Oct 1;57(8):1415–49. [Google Scholar]
  • 33.Christensen KM. United States -- Massachusetts: University of Massachusetts Boston; 2021. A Systematic Review and Meta-Analysis of the Effects of After-School Programs on Academic, Social, Behavioral, Mental Health, and Identity Outcomes Among Youth With Marginalized Identities [Internet] [Ph.D.] [cited 2024 Mar 10]. Available from: https://www.proquest.com/docview/2576924668/abstract/22D55792C3FF489DPQ/1. [Google Scholar]
  • 34.Carrillo JE, Carrillo VA, Perez HR, Salas-Lopez D, Natale-Pereira A, Byron AT. Defining and Targeting Health Care Access Barriers. J Health Care Poor Underserved. 2011;22(2):562–75. doi: 10.1353/hpu.2011.0037. [DOI] [PubMed] [Google Scholar]
  • 35.Schueller SM, Hunter JF, Figueroa C, Aguilera A. Use of Digital Mental Health for Marginalized and Underserved Populations. Curr Treat Options Psychiatry. 2019 Sep 1;6(3):243–55. [Google Scholar]
  • 36.Bergin AD, Vallejos EP, Davies EB, Daley D, Ford T, Harold G, et al. Preventive digital mental health interventions for children and young people: a review of the design and reporting of research. Npj Digit Med. 2020 Oct 15;3(1):1–9. doi: 10.1038/s41746-020-00339-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Côté SM, Boivin M, Liu X, Nagin DS, Zoccolillo M, Tremblay RE. Depression and anxiety symptoms: onset, developmental course and risk factors during early childhood. J Child Psychol Psychiatry. 2009;50(10):1201–8. doi: 10.1111/j.1469-7610.2009.02099.x. [DOI] [PubMed] [Google Scholar]
  • 38.Whalen DJ, Sylvester CM, Luby JL. Depression and Anxiety in Preschoolers: A Review of the Past 7 Years. Child Adolesc Psychiatr Clin N Am. 2017 Jul;26(3):503–22. doi: 10.1016/j.chc.2017.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Teens Atske S. Social Media and Technology 2022 [Internet] Pew Research Center: Internet, Science & Tech. 2022. [cited 2023 Mar 14]. Available from: https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/
  • 40.Center PR. 1. Children’s engagement with digital devices, screen time [Internet] Pew Research Center: Internet, Science & Tech. 2020. [cited 2024 Mar 17]. Available from: https://www.pewresearch.org/internet/2020/07/28/childrens-engagement-with-digital-devices-screen-time/
  • 41.Loke L, Blishen A, Gray C, Ahmadpour N. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems [Internet] New York, NY, USA: Association for Computing Machinery; 2021. Safety, Connection and Reflection: Designing with Therapists for Children with Serious Emotional Behaviour Issues. [cited 2024 Mar 18]. p. 1–17. (CHI ’21). Available from: https://dl.acm.org/doi/10.1145/3411764.3445178. [Google Scholar]
  • 42.Lehtimaki S, Martic J, Wahl B, Foster KT, Schwalbe N. Evidence on Digital Mental Health Interventions for Adolescents and Young People: Systematic Overview. JMIR Ment Health. 2021 Apr 29;8(4):e25847. doi: 10.2196/25847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.d’Halluin A, Costa M, Morgiève M, Sebbane D. Attitudes of Children, Adolescents, and Their Parents Toward Digital Health Interventions: Scoping Review. J Med Internet Res. 2023 May 2;25(1):e43102. doi: 10.2196/43102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Clark LH, Hudson JL, Dunstan DA, Clark GI. Capturing the Attitudes of Adolescent Males’ Towards Computerised Mental Health Help-Seeking. Aust Psychol. 2018;53(5):416–26. [Google Scholar]
  • 45.Dang MT. Social Connectedness and Self-Esteem: Predictors of Resilience in Mental Health among Maltreated Homeless Youth. Issues Ment Health Nurs. 2014 Mar 1;35(3):212–9. doi: 10.3109/01612840.2013.860647. [DOI] [PubMed] [Google Scholar]
  • 46.Bagot K, Hodgdon E, Sidhu N, Patrick K, Kelly M, Lu Y, et al. End User–Informed Mobile Health Intervention Development for Adolescent Cannabis Use Disorder: Qualitative Study. JMIR MHealth UHealth. 2019 Oct 4;7(10):e13691. doi: 10.2196/13691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Nguyen L, Huang LN, Arganza GF, Liao Q. The influence of race and ethnicity on psychiatric diagnoses and clinical characteristics of children and adolescents in children’s services. Cultur Divers Ethnic Minor Psychol. 2007;13(1):18–25. doi: 10.1037/1099-9809.13.1.18. [DOI] [PubMed] [Google Scholar]

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