datasets collected by government agencies and designs its own survey to capture data elements needed for planning children’s health services. For the 1999–2000 health survey done by the Department of Health, 6,000 interviews of parents with children under age 18 were completed in a county with 1.3 million families with children. The sampling for this survey was designed to provide estimates for the service planning areas of the county. While only large communities like Los Angeles may have the resources for such a data system, they also must deal with such challenges as collecting data from many agencies, schools, and municipalities, as well as with geocoding data according to service planning areas or the equivalent (see Box 6-1).
The MassCHIP system in Massachusetts provides data on the web that can be queried at various substate levels. The data come from multiple state agencies as well as some sources external to government. Data include expanded Behavioral Risk Factor Surveillance System data on children and families that can be aggregated at various substate levels. MassCHIP also produces reports on the Kids Count indicators and the Maternal and Child Health Healthy People 2010 objective for each city, town, and region of the state (see Box 6-2).
Many states or communities produce periodic reports, sometimes referred to as “report cards,” modeled on the types of indicators identified by the Federal Interagency Task Force on Child and Family Statistics or the KIDS COUNT initiative. In some cases, these reports are produced by government agencies and in other cases by community groups. For example, Philadelphia Safe and Sound is an organization dedicated to improving the lives of Philadelphia’s children through “a committed public-private collaborative” and publication of Report Card: The Well-Being of Children and Youth in Philadelphia, which monitors key indicators of childhood health, safety, and development (Philadelphia Safe and Sound, 2001).
There is a growing interest in building data systems that are more sophisticated and have greater potential for policy and research purposes by linking individual-level data from two or more datasets. Linking datasets using consistent individual identifiers significantly increases the benefits of a system, even if personal identifiers are removed after the linkage. For example, linking would allow determination of whether conditions or behaviors co-occur, in what proportion of children, and in which areas of the state or locality. The advantage of this model is that it offers an opportunity to identify clusters of children who have specific characteristics, traits, behaviors, and health conditions and to follow individual children in a population longitudinally. This type of data system, if it provided valid and reliable data on a large and diverse population of children, could allow all the functionalities (surveillance, monitoring, forecasting, indicators to be used for accountability or quality improvement, and research) envi-