developers, other organizations, including federally funded technical assistance centers (see Box 12-2) and state-level organizations (Pennsylvania Commission on Crime and Delinquency;1 Neal, Altman and Burritt, 2003; New York State Office of Mental Health2) are providing assistance in implementing prevention programs, particularly substance abuse and violence prevention programs. In addition, programs are being encouraged to provide manuals and other materials to assist in the implementation of programs.
Data systems that integrate family, school, and developmental information could be a useful tool for targeting and monitoring prevention programs. NCLB legislation has provided an as yet unattained opportunity to use academic information to inform prevention needs. Integration of service and data systems for early childhood health, learning, and mental health, for example, could reduce duplication of services, track families across systems, identify children and families who are particularly vulnerable, link family need levels to services, and assess delivery and outcomes for diverse families in particular communities (Knitzer and Lefkowitz, 2006; Schorr and Marchand, 2007).
Such integration has recently become a policy focus in the early childhood field. For example, the Maternal and Child Health Bureau’s Early Childhood Comprehensive Systems initiative funds states (currently 47) to help coordinate services related to early health care, education, mental health, and family support (Johnson and Theberge, 2007). Studies in other state policy areas (e.g., state-level expenditures on state prekindergarten related to children’s cognitive and social-emotional outcomes in the Early Childhood Longitudinal Study-Kindergarten Cohort; Magnuson, Ruhm, and Waldfogel, 2007) could be applied to future studies on state variation in early childhood policies, such as systems coordination. Controlling for other unobserved state policy characteristics, such as concurrent policy change in other areas, is a challenge. In general, evaluation and quality improvement approaches require adequate data collection, storage, and analysis.
An intervention may be poorly implemented due to a community’s being overly optimistic about its capacity to provide the intervention to