title: Predicting admissions and time spent in hospital over a decade in a population-based record linkage study: the EPIC-Norfolk cohort.
abstract: OBJECTIVE
  To quantify hospital use in a general population over 10 years follow-up and to examine related factors in a general population-based cohort.
  
  
  METHODS
  A prospective population-based study of men and women.
  
  
  METHODS
  Norfolk, UK.
  
  
  METHODS
  11,228 men and 13,786 women aged 40-79 years in 1993-1997 followed between 1999 and 2009.
  
  
  METHODS
  Number of hospital admissions and total bed days for individuals over a 10-year follow-up period identified using record linkage; five categories for admissions (from zero to highest ≥ 7) and hospital bed days (from zero to highest ≥ 20 nights).
  
  
  RESULTS
  Over a period of 10 years, 18,179 (72.7%) study participants had at least one admission to hospital, 13.8% with 7 or more admissions and 19.9% with 20 or more nights in hospital. In logistic regression models with outcome ≥ 7 admissions, low education level OR 1.14 (1.05 to 1.24), age OR per 10-year increase 1.75 (1.67 to 1.82), male sex OR 1.32 (1.22 to 1.42), manual social class 1.22 (1.13 to 1.32), current cigarette smoker OR 1.53 (1.37 to 1.71) and body mass index >30 kg/m² OR 1.41 (1.28 to 1.56) all independently predicted the outcome with p<0.0001. Results were similar for those with ≥ 20 hospital bed days. A risk score constructed using male sex, manual social class, no educational qualifications; current smoker and body mass index >30 kg/m², estimated percentages of the cohort in the categories of admission numbers and hospital bed days in stratified age bands with twofold to threefold differences in future hospital use between those with high-risk and low-risk scores.
  
  
  CONCLUSIONS
  The future probability of cumulative hospital admissions and bed days appears independently related to a range of simple demographic and behavioural indicators. The strongest of these is increasing age with high body mass index and smoking having similar magnitudes for predicting risk of future hospital usage.
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Robert
authors.identifier: http://orcid.org/0000-0002-5088-6343
authors.initials: R
authors.lastname: Luben
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: Shabina
authors.initials: S
authors.lastname: Hayat
authors.affiliation: Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK.
authors.forename: Nicolas
authors.identifier: http://orcid.org/0000-0003-1422-2993
authors.initials: N
authors.lastname: Wareham
authors.affiliation: Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
authors.forename: K T
authors.initials: KT
authors.lastname: Khaw
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D015992:Body Mass Index; D003710:Demography; D004522:Educational Status; D005260:Female; D015438:Health Behavior; D006760:Hospitalization; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay; D008019:Life Style; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010343:Patient Admission; D011446:Prospective Studies; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking / Q000009:adverse effects*; D012923:Social Class; D006113:United Kingdom
keywords: EPIDEMIOLOGY; HEALTH SERVICES ADMINISTRATION & MANAGEMENT; PUBLIC HEALTH