title: Using a mailed survey to predict hospital admission among patients older than 80.
abstract: OBJECTIVE
  To determine if the results of a questionnaire mailed to older patients can help identify those patients at greatest risk of hospital admission.
  
  
  METHODS
  A longitudinal cohort study.
  
  
  METHODS
  A prepaid managed care plan in the Denver metropolitan area.
  
  
  METHODS
  Of the 4414 eligible patients at least 81 years old, 3745 (84.8%) responded.
  
  
  METHODS
  We studied the predictive power of self-reported demographic, health status, medical history, health habits, functional status (including Katz' activities of daily living and OARS instrumental activities of daily living), and socioeconomic status data to identify those older adults at greatest risk of hospitalization within 4.5 months of completing the survey. We derived our predictive model on one-half the subjects and tested its validity it on the other half.
  
  
  RESULTS
  Univariate analysis revealed 25 variables significantly associated with hospital admission. In a logistic regression model, four significant variables successfully stratified the patients by risk of admission. These four variables are: the presence of heart disease, the presence of diabetes, need for help preparing meals, and limited physical independence (requiring the help of a person or mechanical aid to get around). In addition, there was an antagonistic interaction between the presence of heart disease and limited physical independence. The model stratified patients from low risk (4.5% chance of admission) to high risk (39% chance of admission). As measured by the Hosmer-Lemeshow statistic and the area under a receiver-operator characteristic (ROC) curve, this model fit both the derivation and validation subjects well.
  
  
  CONCLUSIONS
  A mailed questionnaire achieved a high response rate, and the information collected produced an effective model predictive of hospitalization in the short term. Four easily ascertained pieces of information identify those patients older than age 81 at increased risk.
authors.affiliation: East Boston Neighborhood Health Center, Massachusetts 02128, USA.
authors.forename: J D
authors.initials: JD
authors.lastname: Freedman
authors.forename: A
authors.initials: A
authors.lastname: Beck
authors.forename: B
authors.initials: B
authors.lastname: Robertson
authors.forename: B N
authors.initials: BN
authors.lastname: Calonge
authors.forename: G
authors.initials: G
authors.lastname: Gade
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000369:Aged, 80 and over / Q000706:statistics & numerical data*; D000704:Analysis of Variance; D005260:Female; D015577:Geriatric Assessment; D006304:Health Status; D006760:Hospitalization; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D011795:Surveys and Questionnaires / Q000592:standards*