Linking individual medicare health claims data with work-life claims and other administrative data.

<bold>Background: </bold>Researchers investigating health outcomes for populations over age 65 can utilize Medicare claims data, but these data include no direct information about individuals' health prior to age 65 and are not typically linkable to files containing data on exposures and behaviors during their worklives. The current paper is a proof-of-concept, of merging employers' administrative data and private, employment-based health claims with Medicare data. Characteristics of the linked data, including sensitivity and specificity, are evaluated with an eye toward potential uses of such linked data. This paper uses a sample of former manufacturing workers from an industrial cohort as a test case. The dataset created by this integration could be useful to research in areas such as social epidemiology and occupational health.<bold>Methods: </bold>Medicare and employment administrative data were linked for a large cohort of manufacturing workers (employed at some point during 1996-2008) who transitioned onto Medicare between 2001-2009. Data on work-life health, including biometric indicators, were used to predict health at age 65 and to investigate the concordance of employment-based insurance claims with subsequent Medicare insurance claims.<bold>Results: </bold>Chronic diseases were found to have relatively high levels of concordance between employment-based private insurance and subsequent Medicare insurance. Information about patient health prior to receipt of Medicare, including biometric indicators, were found to predict health at age 65.<bold>Conclusions: </bold>Combining these data allows for evaluation of continuous health trajectories, as well as modeling later-life health as a function of work-life behaviors and exposures. It also provides a potential endpoint for occupational health research. This is the first harmonization of its kind, providing a proof-of-concept. The dataset created by this integration could be useful for research in areas such as social epidemiology and occupational health. [ABSTRACT FROM AUTHOR]