Ways to Reduce the Risk That Youths in Foster Care Will Become Homeless After Aging Out - Research Paper

Objectives. We estimated the incidence of homelessness during the transition to adulthood and identified the risk and protective factors that predict homelessness during this transition.

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Methods. Using data from the Midwest Evaluation of the Adult Functioning of Former Foster Youth, a longitudinal study of youths aging out of foster care in 3 Midwestern states, and a bounds approach, we estimated the cumulative percentage of youths who become homeless during the transition to adulthood. We also estimated a discrete time hazard model that predicted first reported episode of homelessness.

Results. Youths aging out of foster care are at high risk for becoming homeless during the transition to adulthood. Between 31% and 46% of our study participants had been homeless at least once by age 26 years. Running away while in foster care, greater placement instability, being male, having a history of physical abuse, engaging in more delinquent behaviors, and having symptoms of a mental health disorder were associated with an increase in the relative risk of becoming homeless.

Conclusions. Policy and practice changes are needed to reduce the risk that youths in foster care will become homeless after aging out.

Introduction

Among the populations at greatest risk for becoming homeless are the 25 000 to 30 000 youths who age out of foster care each year when they turn 18 or, in some states, 21.1 Unlike many of their peers who continue to live with or receive financial assistance from their parents, these youths often struggle just to keep themselves housed.2,3 A review of research published between 1990 and 2011 has suggested that between 11% and 36% of the youths who age out of foster care become homeless during the transition to adulthood.4-6 By comparison, approximately 4% of the nationally representative sample of youths aged 18 to 26 years who participated in the third wave of the National Longitudinal Study of Adolescent Health reported ever being homeless.7Youths who become homeless after aging out of foster care appear to experience many of the same problems as other homeless youths and young adults,8-12 including high rates of mental health disorders, a high risk of physical or sexual victimization, and a lack of access to health care services.5,6,13 Although researchers have compared youths who became homeless after aging out of foster care with youths who aged out but did not become homeless, not much is known about which youths are at greatest risk of becoming homeless after aging out because these studies used a cross-sectional design.5,6To our knowledge, only 1 study of homelessness among youths aging out of foster care used longitudinal data to identify risk and protective factors. Dworsky and Courtney14 analyzed data collected from youths transitioning out of foster care in 3 Midwestern states. All other things being equal, the odds of becoming homeless by age 19 years were higher for those who (1) had run away more than once while in foster care, (2) were placed in a group care setting at baseline, (3) had been physically abused before entering foster care, (4) had engaged in more delinquent behaviors, and (5) did not feel very close to a biological parent or grandparent.

We built on Dworsky and Courtney's14 analysis and asked what the predictors of becoming homeless would be when (1) the observation period was extended to age 26 years, (2) event history techniques were used to measure relative risk, and (3) the sample included youths who were still in foster care when the data Dworsky and Courtney analyzed were collected. This third condition is important because 69% of the study participants did not exit foster care until sometime between their 20th and 21st birthdays.

Our analysis was based on data from the Midwest Evaluation of the Adult Functioning of Former Foster Youth (the Midwest Study), which followed a sample of youths from Iowa, Wisconsin, and Illinois for 10 years beginning in 2002. The sampling frame included all the Iowa and Wisconsin youths and two thirds of the Illinois youths who had entered foster care before their 16th birthday, were still in foster care at age 17 years, and had been removed from home for reasons other than delinquency; 96% of those eligible were interviewed at baseline (n = 732). Follow-up data were collected at ages 19, 21, 23 or 24, and 26 years, with response rates ranging between 81% and 83%. (Additional information about the design of the study can be found at http://www.chapinhall.org/research/report/midwest-evaluation-adult-functioning-former-foster-youths.) Because Illinois is the only 1 of the 3 Midwest Study states in which youths can and do routinely remain in foster care until their 21st birthday, 73% of the Illinois participants who were interviewed at wave 2 were still in foster care when the second wave of data were collected compared with fewer than 1% of the participants from Iowa and Wisconsin.

At each postbaseline wave of data collection, respondents were asked whether they had "ever been homeless for at least one night since we last talked to you." We defined "homeless" as "sleep[ing] in a place where people weren't meant to sleep, or sleep[ing] in a homeless shelter, or [not having] a regular residence in which to sleep." We used responses to this question to estimate the discrete-time hazard of becoming homeless for the first time. The discrete-time hazard represents the conditional probability that respondent i will become homeless between waves j and j + 1 given that the respondent has never been homeless before, that is, in which h is the discrete time hazard, Pr is the conditional probability and T is the failure time.15 It is appropriate to use when data are interval censored, as they are in this case, where we know the period during which respondents became homeless but not the exact date.16To estimate the discrete-time hazard, we created a file of person-period records (i.e., 1 record per respondent per postbaseline wave of data collected, with a maximum of 4) that included a binary indicator coded "1" if the respondent reported being homeless for at least 1 night since the most recent interview and "0" otherwise. We treated observations as right censored if the respondent was permanently lost to attrition (i.e., data were not collected at any subsequent wave) or if it was the last record for a respondent who had never been homeless.

We modeled the discrete-time hazard as a function of time since the baseline interview and a vector of covariates. Our choice of covariates was guided by prior research on homeless youths, our knowledge about youths aging out of foster care, and the results reported by Dworsky and Courtney.14 They included time-invariant covariates that were only measured at baseline: gender (1 = male; 0 = female), race (2 dummies: White and other, with African American as the reference group), state (Iowa or Wisconsin = 1, Illinois = 0), physically abused before foster care placement (1 = yes, 0 = no), sexually abused before foster care placement (1 = yes, 0 = no), total number of foster care placements (continuous), ever ran away from a foster care placement (1 = yes, 0 = no), placed in group care at baseline (1 = yes, 0 = no), and placed with a relative at baseline (1 = yes, 0 = no). They also included time-varying covariates that were measured at each wave: educational attainment (2 dummies: "completed high school but no college" and "completed at least 1 year of college," with "did not complete high school" as the reference group), currently employed (1 = yes, 0 = no), sexual orientation (1 = not 100% heterosexual, 0 = 100% heterosexual), symptoms of depression or posttraumatic stress disorder (1 = yes, 0 = no), symptoms of an alcohol or other drug use disorder (1 = yes, 0 = no), number of delinquent behaviors (continuous), social support (continuous), very close to a biological parent or grandparent (1 = yes, 0 = no), ever incarcerated since prior interview (1 = yes, 0 = no), and the number of months between interviews (continuous). All the time-varying covariates were lagged so that the values at wave j were used to predict homelessness during the interval between waves jand j + 1.

Time was represented by a set of g 1 dummies, where g is the total number of intervals between waves. Each interval has its own baseline hazard, determined by the parameter estimate for its corresponding dummy together with the overall intercept.17 The model also includes 3 terms representing interactions between state and time to test whether the conditional probability of becoming homeless during interval gwas different in Illinois than in Iowa or Wisconsin.

We used the SAS 9.2 Multiple Imputation procedure (SAS Institute, Cary, NC) to deal with missing covariate values, which involved filling in the missing data m times to create m complete data sets, analyzing each of the m complete data sets using standard statistical procedures, and combining the results from those m analyses to generate valid statistical inferences about the parameters.18 We used the default value of m, which is 5.

Although discrete-time models predict the conditional probability of an event's occurrence, the parameter estimates are directly comparable to Cox proportional hazard model coefficients, and the exponentiated coefficients can be interpreted as hazard ratios when the complementary log-log link (i.e., log[log(1 lij)] = aj + vx) is used (in which lij is the discrete time hazard, v is a vector of coefficients, and aj is a constant related to the conditional survival probability in the interval; F. Steele, unpublished manuscript, 2009).15We focused our analysis on the 624 respondents (i.e., 85% of the baseline sample) for whom we were able to observe whether they became homeless postbaseline. This sample included 435 for whom we had 5 waves of survey data plus 27 who reported having been homeless before being lost to attrition and 162 who were interviewed after missing 1 or more waves of data collection. We coded 40% (n = 65) of the 162 respondents as having been homeless starting with the first wave for which they were missing data because they reported an episode of homelessness the next time they were interviewed.

The 108 respondents whose outcomes we did not observe had either not completed any follow-up interviews (n = 19) or were permanently lost to attrition before becoming homeless (n = 89). An analysis of attrition revealed that these 108 respondents were more likely to be male and more likely to be from Illinois than the 624 respondents whose outcomes were observed.

The thick solid line in Figure 1 shows that 36% of the 624 respondents whose outcomes we observed had been homeless at least once by age 26 years. The true percentage could be higher or lower than this figure suggests depending on what happened to the 108 respondents whose outcomes we did not observe. On the one hand, if we assume a best-case scenario, namely that none of those respondents became homeless, then the true value would be 31% (the dashed line in Figure 1). On the other hand, if we assume a worst-case scenario, namely that all of those respondents became homeless, then the true value would be 46% (the dotted line in Figure 1). Neither of these 2 extremes is likely to have occurred, but the true value must lie somewhere in between.