摘要：Objectives. To assess the mental health effects on adolescents of low-income families residing in high-poverty public housing who received housing vouchers to assist relocation. Methods. We defined treatment effects to compare 2829 adolescents aged 12 to 19 years in families offered housing vouchers versus those living in public housing in the Moving to Opportunity experiment (1994–1997; Boston, MA; Baltimore, MD; Chicago, IL; Los Angeles, CA; New York, NY). We employed model-based recursive partitioning to identify subgroups with heterogeneous treatment effects on psychological distress and behavior problems measured in 2002. We tested 35 potential baseline treatment modifiers. Results. For psychological distress, Chicago participants experienced null treatment effects. Outside Chicago, boys experienced detrimental effects, whereas girls experienced beneficial effects. Behavior problems effects were null for adolescents who were aged 10 years or younger at baseline. For adolescents who were older than 10 years at baseline, violent crime victimization, unmarried parents, and unsafe neighborhoods increased adverse treatment effects. Adolescents who were older than 10 years at baseline without learning problems or violent crime victimization, and whose parents moved for better schools, experienced beneficial effects. Conclusions. Health effects of housing vouchers varied across subgroups. Supplemental services may be necessary for vulnerable subgroups for whom housing vouchers alone may not be beneficial. Moving to Opportunity (MTO) was a landmark housing demonstration sponsored by the US Department of Housing and Urban Development that randomly assigned more than 4000 low-income families to receive housing subsidies to move out of distressed public housing into better housing units and safer neighborhoods. Although the MTO program was not designed with health in mind, it substantially improved the mental health of household heads (mothers) and their adolescent daughters. 1,2 However, MTO had adverse effects on boys’ mental health. 3–5 Assessing such treatment heterogeneity is important for determining subgroups for whom the treatment had unintended negative effects, was ineffective, or was particularly beneficial. This information may in turn guide program eligibility and help identify support services to improve program effectiveness, akin to a policy version of precision medicine’s movement to tailor treatment and services to individual variability. 6 Identifying treatment heterogeneity could also guide changes to future housing subsidy programs and methods of implementation. Treatment heterogeneity of a randomized exposure has traditionally been assessed by testing treatment interaction terms in regression models, typically 1 by 1. The development of methodological approaches that assess multidimensional data patterns has now made it possible to investigate higher-order patterns of treatment modification in which a series of participant characteristics (e.g., age, gender, race) are considered. Machine-learning approaches, such as model-based recursive partitioning, are particularly well suited for detecting complex interactions that may be difficult to isolate with a priori hypotheses specification and traditional regression techniques. 7 With traditional regression methods, investigating higher-order interactions with 35 potential effect modifiers (as we do in this study) would entail specifying a 36-way interaction with treatment as well as different combinations of 2-, 3-, 4-, up to 35-way interactions—totaling more than 68 billion unique interaction terms. 8 Alternatively, by incrementally identifying groups with similar treatment effects, recursive partitioning arguably optimizes the identification of treatment heterogeneity in the data, with a much more parsimonious approach. 9 Model-based recursive partitioning is suitable for large data sets with many variables. Unlike other methods in which the original variables are condensed into a reduced set, thereby no longer permitting examination of individual variables (e.g., principal components analysis or factor analysis), model-based recursive partitioning can process patterns from many variables and still allow the examination of individual variables. 10 In selecting potential treatment modifiers of the MTO housing experiment on adolescent mental health, we used several theoretical models. Residential mobility models posit that the effect of residential mobility depends on a series of characteristics related to one’s history of and preferences for residential moves, including history of migration, reasons for the move, and features of the new neighborhood. 11 We supplemented this theory by hypothesizing that families struggling with chronic stressors, such as health problems, may find the added burden of moving more difficult. Furthermore, social capital theory suggests that social connectedness in the baseline neighborhood may inhibit residential mobility and modify the effects of mobility because of the potential disruption of moving on familial and social network ties. 11 We applied these theories to test for treatment heterogeneity in MTO. We implemented model-based recursive partitioning, which is particularly suited for identifying higher-order interactions in large data sets, although it is rarely applied in public health. We adapted the method to preserve MTO’s experimental design, and therefore the strong internal validity, for inferring how the housing policy affected mental health. We believe this is the first such application of the recursive partitioning method to an experimental design. We replicated our results in subsets of the data to demonstrate robustness.
