title: Risk factors for hospital-acquired 'poor glycemic control': a case-control study.
abstract: OBJECTIVE
  To determine the patient and hospital characteristics associated with severe manifestations of 'poor glycemic control'-a 'no-pay' hospital-acquired condition defined by the US Medicare program based on hospital claims related to severe complications of diabetes.
  
  
  METHODS
  A nested case-control study.
  
  
  METHODS
  California acute care hospitals from 2005 to 2006.
  
  
  METHODS
  All cases (n= 261) with manifestations of poor glycemic control not present on admission admitted to California acute care hospitals from 2005 to 2006 and 261 controls were matched (1:1) using administrative data for age, sex, major diagnostic category and severity of illness.
  
  
  METHODS
  The adjusted odds ratio (OR) for experiencing poor glycemic control.
  
  
  RESULTS
  Deaths (16 vs. 9%, P= 0.01) and total costs ($26,125 vs. $18,233, P= 0.026) were significantly higher among poor glycemic control cases. Risk-adjusted conditional logistic regression revealed that each additional chronic condition increased the odds of poor glycemic control by 12% (OR: 1.12, 95% CI: 1.04-1.22). The interaction of registered nurse staffing and hospital teaching status suggested that in non-teaching hospitals, each additional nursing hour per adjusted patient day significantly reduced the odds of poor glycemic control by 16% (OR: 0.84, 95% CI: 0.73-0.96). Nurse staffing was not significant in teaching hospitals (OR: 0.98, 95% CI: 0.88-1.11).
  
  
  CONCLUSIONS
  Severe poor glycemic control complications are relatively rare but meaningful events with disproportionately high costs and mortality. Increasing nurse staffing may be an effective strategy in reducing poor glycemic control complications particularly in non-teaching hospitals.
authors.affiliation: Center for Health Outcomes and Policy Research, University of Pennsylvania School of Nursing, Philadelphia, PA 19104-4217, USA. mchughm@nursing.upenn.edu
authors.forename: Matthew D
authors.initials: MD
authors.lastname: McHugh
authors.forename: Jingjing
authors.initials: J
authors.lastname: Shang
authors.forename: Douglas M
authors.initials: DM
authors.lastname: Sloane
authors.forename: Linda H
authors.initials: LH
authors.lastname: Aiken
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000032:analysis*; D002140:California; D016022:Case-Control Studies; D015897:Comorbidity; D003920:Diabetes Mellitus / Q000097:blood* / Q000209:etiology*; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006784:Hospitals, Teaching / Q000706:statistics & numerical data; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000209:etiology; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D008875:Middle Aged; D009741:Nursing Staff, Hospital / Q000706:statistics & numerical data; D011787:Quality of Health Care / Q000706:statistics & numerical data*; D012307:Risk Factors; D012720:Severity of Illness Index; D012737:Sex Factors
chemical_list: D001786:Blood Glucose