Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 427975
Title Longitudinal administrative data can be used to examine multimorbidity, provided false discoveries are controlled for
Author(s) Wong, A.; Boshuizen, H.C.; Schellevis, F.G.; Kommer, G.J.; Polder, J.J.
Source Journal of Clinical Epidemiology 64 (2011)10. - ISSN 0895-4356 - p. 1109 - 1117.
DOI http://dx.doi.org/10.1016/j.jclinepi.2010.12.011
Department(s) Chair Nutrition and Disease
Biometris (WU MAT)
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) clinical comorbidity index - general-practice - diseases - health - association - prevalence - disability - databases - morbidity - mortality
Abstract Objective This article presents methods for using administrative data to study multimorbidity in hospitalized individuals and indicates how the findings can be used to gain a deeper understanding of hospital multimorbidity. Study Design and Setting A Dutch nationwide hospital register (n = 4,521,856) was used to calculate age- and sex-standardized observed/expected ratios of disease-pairing prevalences with corresponding confidence intervals. Results The strongest association was found for the combination between alcoholic liver and mental disorders due to alcohol abuse (observed/expected = 39.2). Septicemia was found to cluster most frequently with other diseases. The consistency of the ratios over time depended on the number of observed cases. Furthermore, the ratios also depend on the length of the time frame considered. Conclusion Using observed/expected ratios calculated from the administrative data set, we were able to (1) better quantify known morbidity pairings while also revealing hitherto unnoticed associations, (2) find out which pairings cluster most strongly, and (3) gain insight into which diseases cluster frequently with other diseases. Caveats with this method are finding spurious associations on the basis of too few observed cases and the dependency of the ratio magnitude on the length of the time frame observed
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