- Adoption Disruption, Dissolution, and Supports in North Carolina
- Adoption Subsidies in California
Although most adoptions have positive outcomes for the children and their families, many families need supportive services during some part of their child's development. In response to these needs, many states have developed post-adoption service (PAS) programs and other supports for adoptive families. The U.S. Department of Health and Human Services contracted with RTI to examine these rapidly growing and evolving programs. Research questions included the need for PAS, characteristics of existing programs, and strategies used to assess program effectiveness. RTI, in collaboration with the University of North Carolina at Chapel Hill School of Social Work, conducted a literature review, analysis of secondary data, case studies of five PAS programs, and an assessment of evaluation issues affecting PAS.
This portion of the study explored whether administrative data could be used to better understand the use of subsidies for purchase of services and to describe the disruption, dissolution, and displacement of adoptions. Analyses using data from two states, California and North Carolina, demonstrate what could be done in other states with similar data and suggest how modifications to administrative data systems could enhance our understanding of adoptions.
Adoption Disruption, Dissolution, and Supports in North Carolina
Our analysis combined adoption assistance data with foster care placement records to identify children who either had records of adoption assistance payments or who were identified as having been adopted prior to the foster care placement. These matches were complicated by the use of different ID numbers before and after adoption.
We utilized two lines of analyses to examine adoption dissolution in North Carolina. First, we tracked a cohort of adopted children, using three conditions to define dissolution: (1) date of entry into out-of-home placement occurred at least 90 days after final adoption decree date, (2) adoption assistance was no longer being received after this placement, and (3) if permanency was achieved at end of this placement, it was achieved with someone other than the primary caregiver at time of placement. Of the 8,647 children in the adoption assistance data file, only 70 of these met the dissolution criteria. Older children are significantly more likely to experience dissolution than younger children. Black children are twice as likely as white children to return to placement after an adoption, and about half of dissolutions occur within three years of adoption. Although a dissolution rate of less than 2 percent must be viewed cautiously, it may be plausible in light of the state's relatively low rate of reentry to foster care.
We also looked at all children who entered out-of-home placement to determine whether a child was previously adopted, using a data element added in July 1997 as part of the Adoption and Foster Care Analysis and Reporting System (AFCARS) enhancement. Of the children entering placement between July 1997 and December 2001, 318 had been adopted previously. Compared to children who had not been adopted, they were more likely to be white and to be teenagers. Although these analyses do not provide sufficient data to calculate a dissolution rate, the analyses provide some insight into the characteristics of children who are reentering placement following an adoption.
Using the foster care placement files, we next examined the subject of how many children experienced an adoption disruption, that is, had placements coded as an adoptive home but ultimately were not adopted. Although most of the children placed in adoptive homes exited to adoption or remained in care, 29 percent may have experienced disruptions or had changes in their adoption plans for other reasons. It is important to note that the majority of children (65 percent) who achieve permanency through adoption in North Carolina appear to have been adopted by foster parents, without ever having been identified in the data system as changing status from foster to adoptive homes. Although the data do not support an effort to estimate adoption disruption rates, this may become possible in the future.
Analyses of adoption assistance used records of payments to adopted children (subsidies) or for services received by adopted children (vendor payments). Almost all (94 percent) children with adoption assistance received cash payments, and close to two-thirds (61 percent) also received additional assistance in the form of payments to vendors for therapeutic or medical services or nonrecurring costs of adoption. Nearly all of the children (96 percent) started receiving cash payments within 6 months of the final decree. The average cash payment amount during this time period was $346 per month received for an average of 42 months. Average payments were higher for older children. Just over half of children had no change in their subsidy amounts over the course of their assistance period; for others the increases were not substantial. Multivariate analyses showed that race and age at initial payment were significantly related to the likelihood of a subsidy increase. Even though the model controls for the number of months of assistance, children who begin receiving adoption assistance at a very young age are much more likely to receive increased subsidy payments than older children. Other minority children are less likely to receive an increased subsidy than either white or black children.
Vendor payments also began soon after adoption, with an average of four payments per child, in amounts ranging up to $2,000. Because payments could have been received prior to adoption but recorded under different ID numbers, these numbers are likely to underestimate the amount of vendor payments incurred by an individual child.
Adoption Subsidies in California
Analysis of adoption data sources drew on survey data and administrative records. Survey participants in the California Long-Range Adoption Study (CLAS) completed questionnaires in three waves following adoption of children from foster care in 1988-89, providing information on psychological, social, economic, and relational characteristics. These data were analyzed to examine whether children's behavior is associated with early changes in adoption assistance program (AAP) payments. Half of the children in this sample received AAP funds within two years of placement in their adoptive homes. AAP receipt or nonreceipt tended to remain stable over the subsequent six years of data collection. Youth receiving AAP were much more likely to have Behavior Problem Index (BPI) scores in the clinical range than those who did not receive AAP. Some families manage to care for children with high levels of behavior problems without subsidies, but families are more likely to transition from no subsidy to subsidy because behavior problems increase. The reasons that families stop their subsidy use are less clear.
Administrative data included case records completed at the time of adoption placement for children placed for adoption in 1988-89, and matching AAP records through December 2000. AAP records are updated with each biannual recertification or any time that the AAP amount changes. However, some information is incomplete or missing, and children with many subsidy changes may be overrepresented in the database. Nearly three-quarters had one or two payment changes, the vast majority of which were as a result of recertifications.
Among cases with payment changes, the average amount of each payment change was $95, a meaningful change in comparison with the average monthly payment of $404. Of all payment changes, 26 percent were reductions in payments, which appear to have been made to correct increases that were too high or meant to be temporary. Reasons for AAP changes include changes in the cost of the child's basic care, changes in Medi-Cal coverage, changes in special circumstances, and placement of a child in residential care. Most children entering residential care do so after several payment changes requested by families to help them provide services to their children. This makes the provision of residential care seem somewhat less costly than it would be if this were a common first payment change.
Multivariate analyses were based on the subset of children for whom adoption case record data were available. A limitation of these models is the lack of data representing child disability and behavior problems, which should be related to subsidy amount. The strongest predictor in these models is family income, although not entirely in the direction that would be expected if subsidies were being used to help families meet children's service needs.
Analyses of bivariate associations between changes in subsidy level and adoptive families' demographic characteristics focused on positive payment changes, as events that signal needs (of varying magnitude) within the adoptive family, rather than on the amount of subsidies received over time. Comparing demographic differences in smaller ($0 to $300) and larger ($301 or more) amounts, children adopted by a well-educated adopting mother or in higher-income families were significantly more likely to receive a large amount of subsidy changes. Associations between race and amount of subsidy change were not significant.
Three logistic regression models achieved acceptable, but not impressive, goodness-of-fit results. Event history analyses were then used to examine the timing of payment changes in order to understand patterns of post-adoptive services need. Although only 25 percent of AAP recipients have experienced a payment change before the required two-year subsidy recertification, those with multiple payment changes are likely to experience them more quickly. Families with incomes between $26,443 and $36,000 are significantly more likely to experience a payment change within three years after placement.
Residential treatment has particular policy relevance for states because the federal government will not reimburse for this service. Although only 34 children in this sample entered residential care during the study time frame, a Cox proportional hazards model could be computed. The model shows a higher likelihood of payment changes associated with residential placement for children adopted when older. Most children who entered residential treatment had three or more prior payment changes. Families with incomes between $36,001 and $48,762 were most likely to receive a payment change for residential treatment. Neither race nor the education of the mother was significantly related to the use of subsidies for residential treatment.
Data from the two states differ in availability and structure; however, some clear similarities and differences have emerged. Almost all children adopted from foster care in North Carolina received cash assistance subsidy payments, but amounts tend to remain unchanged or to increase gradually with age. In California, by contrast, there are fewer cases in which there are no changes. As payment changes occur, the rapidity of subsequent changes increases. Thus the number of payment changes provided could help identify families in need of additional assistance. Although CLAS data suggest that subsidy increases are associated with the worsening of children's behavior, we also see that they are strongly associated with parental characteristics. The equitability of adoption subsidy adjustments needs to be better understood.
Data in North Carolina support previous findings of low dissolution rates, with greater risk for older children and for minority children compared with infants and white children in the state. In California, event history analysis showed that the likelihood of entering residential care is associated with age at placement, the number of prior payment changes, and to a lesser extent family income.
Adoption data are highly confidential and fragmented. Data about foster care histories and foster care payment amounts, adoption home studies (or their electronic summaries), adoption subsidy amounts, payments for special services (i.e., vendor payments), and disruptions, dissolutions, or displacements are often collected and stored in unrelated data systems, if at all. Record matching is often required because common identifiers do not exist. Confidentiality of adoption data impeded efforts to link the data in different files. Nevertheless, even with these constraints, the analyses provide an important first look at some critical issues and begin to identify ways in which administrative data files might be modified to support future analyses.
Taken together, the analyses in this document serve several purposes. They offer a sample of the kinds of administrative data that are available to better understand post-adoption services and supports. They offer ideas about the kinds of analyses that can be done to bring meaning to these data. They offer some substantive findings about adoption subsidies and how they are used. Finally, they offer some ideas about modifications to administrative data systems that could improve their usefulness in understanding adoption.