Mortality Risk Prediction by an Insurance Company and Long-Term Follow-Up of 62,000 Men

Eric J G Sijbrands, Erik Tornij and Sietske J Homsma

PLOS ONE, 2009, vol. 4, issue 5, 1-6

Abstract: Background: Insurance companies use medical information to classify the mortality risk of applicants. Adding genetic tests to this assessment is currently being debated. This debate would be more meaningful, if results of present-day risk prediction were known. Therefore, we compared the predicted with the observed mortality of men who applied for life insurance, and determined the prognostic value of the risk assessment. Methods: Long-term follow-up was available for 62,334 male applicants whose mortality risk was predicted with medical evaluation and they were assigned to five groups with increasing risk from 1 to 5. We calculated all cause standardized mortality ratios relative to the Dutch population and compared groups with Cox's regression. We compared the discriminative ability of risk assessments as indicated by a concordance index (c). Results: In 844,815 person years we observed 3,433 deaths. The standardized mortality relative to the Dutch male population was 0.76 (95 percent confidence interval, 0.73 to 0.78). The standardized mortality ratios ranged from 0.54 in risk group 1 to 2.37 in group 5. A large number of risk factors and diseases were significantly associated with increased mortality. The algorithm of prediction was significantly, but only slightly better than summation of the number of disorders and risk factors (c-index, 0.64 versus 0.60, P

Date: 2009
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0005457 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 05457&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0005457

DOI: 10.1371/journal.pone.0005457

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone (plosone@plos.org).