title: Comorbidity and repeat admission to hospital for adverse drug reactions in older adults: retrospective cohort study.
abstract: OBJECTIVE
  To identify factors that predict repeat admission to hospital for adverse drug reactions (ADRs) in older adults.
  
  
  METHODS
  Population based retrospective cohort study.
  
  
  METHODS
  All public and private hospitals in Western Australia.
  
  
  METHODS
  28 548 patients aged >or=60 years with an admission for an ADR during 1980-2000 followed for three years using the Western Australian data linkage system.
  
  
  RESULTS
  5056 (17.7%) patients had a repeat admission for an ADR. Repeat ADRs were associated with sex (hazard ratio 1.08, 95% confidence interval 1.02 to 1.15, for men), first admission in 1995-9 (2.34, 2.00 to 2.73), length of hospital stay (1.11, 1.05 to 1.18, for stays >or=14 days), and Charlson comorbidity index (1.71, 1.46 to 1.99, for score >or=7); 60% of comorbidities were recorded and taken into account in analysis. In contrast, advancing age had no effect on repeat ADRs. Comorbid congestive cardiac failure (1.56, 1.43 to 1.71), peripheral vascular disease (1.27, 1.09 to 1.48), chronic pulmonary disease (1.61, 1.45 to 1.79), rheumatological disease (1.65, 1.41 to 1.92), mild liver disease (1.48, 1.05 to 2.07), moderate to severe liver disease (1.85, 1.18 to 2.92), moderate diabetes (1.18, 1.07 to 1.30), diabetes with chronic complications (1.91, 1.65 to 2.22), renal disease (1.93, 1.71 to 2.17), any malignancy including lymphoma and leukaemia (1.87, 1.68 to 2.09), and metastatic solid tumours (2.25, 1.92 to 2.64) were strong predictive factors. Comorbidities requiring continuing care predicted a reduced likelihood of repeat hospital admissions for ADRs (cerebrovascular disease 0.85, 0.73 to 0.98; dementia 0.62, 0.49 to 0.78; paraplegia 0.73, 0.59 to 0.89).
  
  
  CONCLUSIONS
  Comorbidity, but not advancing age, predicts repeat admission for ADRs in older adults, especially those with comorbidities often managed in the community. Awareness of these predictors can help clinicians to identify which older adults are at greater risk of admission for ADRs and, therefore, who might benefit from closer monitoring.
authors.affiliation: School of Population Health, University of Western Australia, Perth, WA, Australia. min.zhang@uwa.edu.au
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
authors.forename: C D'Arcy J
authors.initials: CD
authors.lastname: Holman
authors.forename: Sylvie D
authors.initials: SD
authors.lastname: Price
authors.forename: Frank M
authors.initials: FM
authors.lastname: Sanfilippo
authors.forename: David B
authors.initials: DB
authors.lastname: Preen
authors.forename: Max K
authors.initials: MK
authors.lastname: Bulsara
mesh_terms: D000367:Age Factors; D000368:Aged; D002908:Chronic Disease; D015897:Comorbidity; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D016896:Treatment Outcome; D014904:Western Australia / Q000453:epidemiology