Studies of Welfare Populations: Data Collection and Research Issues. Data Sharing

06/01/2002

Matched data and data linkage should be distinguished from data sharing(2), which implies a more dynamic and active process of data interchange. Data sharing among agencies refers to methods whereby agencies can obtain access to one another's data about individuals, sometimes immediately but nearly always in a timely fashion. Data sharing offers a number of benefits. If different agencies collect similar data about the same person, the collection process is duplicative for both the agencies and the person. Data sharing therefore can increase efficiencies by reducing the paperwork burden for the government and the individual because basic information about clients only needs to be obtained once. Improved responsiveness is also possible. Data sharing enables agencies and researchers to go beyond individual program-specific interventions to design approaches that reflect the interactive nature of most human needs and problems, reaching beyond the jurisdiction of one program or agency. For example, providing adequate programs for children on welfare requires data about the children from educational, juvenile justice, and child welfare agencies. Data sharing is one way to ensure better delivery of public services and a "one-stop" approach for users of these services. Preis (1999) concluded, in his analysis of California efforts to establish integrated children's mental health programs, that data sharing is essential to good decision making and a prerequisite for service coordination. In fact, "if data cannot be exchanged freely among team members an optimal service and support plan cannot be created" (Preis, 1999:5).

Although data sharing has many benefits, it raises issues regarding privacy and confidentiality. Should data collected for one program be available to another? What are the dangers associated with having online information about participants in multiple programs? Who should have access to these data? How can confidentiality and privacy rights be protected while gaining the benefits of linking program data?

When agencies engage in data sharing, the technical problems of getting matched data for research and policy analysis are easily surmounted because information from a variety of programs is already linked. But matched and linked data sets for research and policy analysis can be created without data sharing, and data matching poses far fewer disclosure risks than data sharing because identifiers only need to be used at the time when data are merged. As soon as records are matched, the identifiers are no longer needed and can be removed. The merged data can be restricted to a small group of researchers, and procedures can be developed to prohibit any decisions from being made about individuals based on the data. Nevertheless, even data matching can lead to concerns about invasions of privacy and breaches of confidentiality.

Both data sharing and data matching require the careful consideration of privacy issues and techniques for safeguarding the confidentiality of individual level data. The starting place for understanding how to attend to these considerations is to review the body of law about privacy and confidentiality and the definitions of key concepts that have developed in the past few decades. After defining the concepts of privacy, disclosure, confidentiality, and informed consent, we then briefly review existing federal privacy and confidentiality laws.

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