Core B is designed to disseminate, and provide technical support and documentation for, data sets required for the individual research projects and for the health-forecasting model in Core C. This will involve using the Internet-II system, or CD-ROM's, for both data dissemination , and to provide documentation for the data disseminated. First the 1982, 1984, 1989, 1994, and 1999 NLTCS, and the coordinated longitudinally linked Medicare Part A and B service use data files for 1982 to 2000 (2002), will be available for all P01 investigators. There is also considerable technical documentation on measurement non-response, and sample weight calculations for the NLTCS. The longitudinal file will be updated as additional survey (e.g., the 1999 NLTCS caregiver survey; 1996 MEPS; genetic data for the NLTCS sample) and Medicare service use data file extensions become available. Second, access will be provided to other health surveys, epidemiological, and clinical data files along with documentation produced in a user friendly format. Several health survey data sets will have immediate uses. The first is the 1996 Medical Expenditure Panel Survey (MEPS) which has four components, i.e., the Household Component, the Medical Provider Component, the Insurance Component and the Nursing Home Component. This is the third survey (sponsored by AHCPR) on the financing and use of medical care in the U.S. The other two health expenditure surveys (also to be maintained in Core B) are the NMCES (1977) and NMES (1987), MES uses an overlapping panel design where, after a preliminary contact, five interviews are conducted over 2.5 years. The Medical Provider component contacts physicians and pharmacies identified in the Household component. Thus, the survey provides (for both community and institutional samples) detailed data on drug use and costs. In addition to other health surveys, data will be made available for diagnoses of interest from the Duke Clinical Research Institute (DRCI) which specializes in the conduct of "mega" clinical trials on cardiovascular disease. This involves preparing analytically focused data extracts, more than actual data base management.