House Level Latent Classes as Predictors of Recovery and Evictions.

The current study explored whether substance abuse recovery houses could be categorized into meaningful classes, which might be associated with house evictions as well as changes in individual-level recovery capital. A total of 602 individuals from 42 recovery homes were followed for up to 6 data collection periods over 2 years. House level latent class analyses were based on house-level data. A 3-class model fit very well (entropy 0.94) and better than a 2-class model. Class profiles examined concurrent (averaged across waves 1 and 2) house and resident-level variables (e.g., gender, race, age, employment, education). Class was then used to prospectively predict outcomes of the hazard of eviction and improvement in a recovery index over waves 3-6. One latent class representing 45% of the recovery houses had the highest density of members willing to loan, able to pay their rent, active involvement in outside chapter activities-this group of houses had the best outcomes including the lowest eviction rate and highest mean recovery factor. The two other classes had higher eviction rates, with one having the lowest density of friendship, selectivity of residents, and ability to pay rent. The other of the higher eviction-rate classes surprisingly had the highest density of friendship and advice seeking, but the lowest density of willingness to loan. These findings suggest that there are meaningful differences in types of recovery homes, and that house characteristics appear to influence recovery changes and eviction outcomes.