Modeling and detecting potentially ruinous streaks in health expenditures

Joe Koopmeiners, Bryan E Dowd, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review


The mean of a distribution of medical expenditures in an insured population can be affected significantly by the occurrence of a few high cost cases. This fact leads some organizations that hold the primary risk for the population (e.g., health plans or self-insured employers) to seek reinsurance arrangements that spread the risk of high cost cases across a broader pool. Recently, the private reinsurance market has experienced some difficulties, attributable to information asymmetries between primary risk holders and reinsurers. The disproportionate effect of a few high cost cases also has generated interest in the development of "risk-adjustment" systems that attempt to reduce the difference in health plans' unreimbursed costs either to endogenous management decisions or random chance. We discuss these issues in light of a well-known statistical result regarding the probability of "streaks" in random data. We illustrate problems that can arise and suggest methods to distinguish random streaks from systematic trends.

Original languageEnglish (US)
Pages (from-to)23-42
Number of pages20
JournalInternational journal of health care finance and economics
Issue number1
StatePublished - Mar 2007

Bibliographical note

Funding Information:
Acknowledgments The authors would like to thank Prof. John Nyman for stimulating and enlightening discussions that formed the impetus for this work. The work of the first and third authors was supported in part by NIH grant 1-R01-CA95955-01.


  • Autocorrelation
  • Bayesian methods
  • Reinsurance
  • Risk
  • Risk-adjustment


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