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Abstract:
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Linked survey and administrative data can be used to facilitate richer analyses by augmenting the information collected from the surveys with vital or administrative data. However, the quality of linked data is only as good as the algorithm used to produce them. The National Center for Health Statistics (NCHS) has a data linkage program that is designed to expand the analytic utility of the Center's population-based surveys. The NCHS Data Linkage Program links its health survey data with vital statistics and administrative data sources. However, there has been a growing reluctance of survey participants to provide personally identifiable information (PII). Therefore, in recent years, changes to survey design have been implemented to reduce the amount of PII collected. This, in turn, has limited the information available for data linkages. To address this issue, the Data Linkage Program at NCHS has altered some linkage algorithms. This talk will describe new approaches and compare new and old methodologies, using actual examples from the NCHS Data Linkage Program. The results will be discussed in terms of implications for analyses and future directions of the Data Linkage Program.
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