title: Targeting a high-risk group for fall prevention: strategies for health plans.
abstract: OBJECTIVE
  Although Medicare has implemented incentives for health plans to reduce fall risk, the best way to identify older people at high risk of falling and to use screening results to target fall prevention services remains unknown. We evaluated 4 different strategies using a combination of administrative data and patient-reported information that health plans could easily obtain.
  
  
  METHODS
  Observational study.
  
  
  METHODS
  We used data from 1776 patients 75 years or older in 4 community-based primary care practices who screened positive for a fear of falling and/or a history of falls. For these patients, we predicted fall-related injuries in the 24 months after the date of screening using claims/encounter data. After controlling for age and gender, we predicted the number of fall-related injuries by adding Elixhauser comorbidity count, any claim for a fall-related injury during the 12 months prior to screening, and falls screening question responses in a sequential fashion using negative binomial regression models.
  
  
  RESULTS
  Basic patient characteristics, including age and Elixhauser comorbidity count, were strong predictors of fall-related injury. Among falls screening questions, a positive response to, "Have you fallen 2 or more times in the past year?" was the most predictive of a fall-related injury (incidence rate ratio [IRR], 1.56; 95% CI, 1.25-1.94). Prior claim for a fall-related injury also independently predicted this type of injury (IRR, 1.41; 95% CI, 1.05-1.89). The best model for predicting fall-related injuries combined all of these approaches.
  
  
  CONCLUSIONS
  The combination of administrative data and a simple screening item can be used by health plans to target patients at high risk for future fall-related injuries.
authors.affiliation: David Geffen School of Medicine at UCLA, Division of Geriatrics, 10945 Le Conte Ave, Ste 2339, Los Angeles, CA 90095-1687. E-mail: lajennings@mednet.ucla.edu.
authors.forename: Lee A
authors.initials: LA
authors.lastname: Jennings
authors.forename: David B
authors.initials: DB
authors.lastname: Reuben
authors.forename: Sung-Bou
authors.initials: SB
authors.lastname: Kim
authors.forename: Emmett
authors.initials: E
authors.lastname: Keeler
authors.forename: Carol P
authors.initials: CP
authors.lastname: Roth
authors.forename: David S
authors.initials: DS
authors.lastname: Zingmond
authors.forename: Neil S
authors.initials: NS
authors.lastname: Wenger
authors.forename: David A
authors.initials: DA
authors.lastname: Ganz
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D011320:Primary Health Care / Q000379:methods*; D018570:Risk Assessment