title: The Avalanche Hypothesis and Compression of Morbidity: Testing Assumptions through Cohort-Sequential Analysis.
abstract: BACKGROUND
  The compression of morbidity model posits a breakpoint in the adult lifespan that separates an initial period of relative health from a subsequent period of ever increasing morbidity. Researchers often assume that such a breakpoint exists; however, this assumption is hitherto untested.
  
  
  OBJECTIVE
  To test the assumption that a breakpoint exists--which we term a morbidity tipping point--separating a period of relative health from a subsequent deterioration in health status. An analogous tipping point for healthcare costs was also investigated.
  
  
  METHODS
  Four years of adults' (N = 55,550) morbidity and costs data were retrospectively analyzed. Data were collected in Pittsburgh, PA between 2006 and 2009; analyses were performed in Rochester, NY and Ann Arbor, MI in 2012 and 2013. Cohort-sequential and hockey stick regression models were used to characterize long-term trajectories and tipping points, respectively, for both morbidity and costs.
  
  
  RESULTS
  Morbidity increased exponentially with age (P<.001). A morbidity tipping point was observed at age 45.5 (95% CI, 41.3-49.7). An exponential trajectory was also observed for costs (P<.001), with a costs tipping point occurring at age 39.5 (95% CI, 32.4-46.6). Following their respective tipping points, both morbidity and costs increased substantially (Ps<.001).
  
  
  CONCLUSIONS
  Findings support the existence of a morbidity tipping point, confirming an important but untested assumption. This tipping point, however, may occur earlier in the lifespan than is widely assumed. An "avalanche of morbidity" occurred after the morbidity tipping point-an ever increasing rate of morbidity progression. For costs, an analogous tipping point and "avalanche" were observed. The time point at which costs began to increase substantially occurred approximately 6 years before health status began to deteriorate.
authors.affiliation: School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, United States of America.
authors.forename: Jordan
authors.initials: J
authors.lastname: Silberman
authors.affiliation: Wellness & Prevention, Inc., Ann Arbor, Michigan, United States of America.
authors.forename: Chun
authors.initials: C
authors.lastname: Wang
authors.affiliation: Wellness & Prevention, Inc., Ann Arbor, Michigan, United States of America; School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Shawn T
authors.initials: ST
authors.lastname: Mason
authors.affiliation: SocialWellth, Inc., Las Vegas, Nevada, United States of America.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Schwartz
authors.affiliation: Highmark, Inc., Pittsburgh, Pennsylvania, United States of America.
authors.forename: Matthew
authors.initials: M
authors.lastname: Hall
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, United States of America.
authors.forename: Jason L
authors.initials: JL
authors.lastname: Morrissette
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, United States of America; Center of Excellence at Canandaigua, Canandaigua VA Medical Center, Canandaigua, New York, United States of America.
authors.forename: Xin M
authors.initials: XM
authors.lastname: Tu
authors.affiliation: Wellness & Prevention, Inc., Ann Arbor, Michigan, United States of America.
authors.forename: Janet
authors.initials: J
authors.lastname: Greenhut
mesh_terms: D000328:Adult; D015331:Cohort Studies; D005260:Female; D017048:Health Care Costs; D006801:Humans; D008297:Male; D008824:Michigan; D008875:Middle Aged; D015233:Models, Statistical; D009017:Morbidity / Q000639:trends*; D009518:New York; D012189:Retrospective Studies; D055815:Young Adult