title: Impact of patient characteristics on the risk of influenza/ILI-related complications.
abstract: BACKGROUND
  We sought to quantify the impact of patient characteristics on complications and health care costs associated with influenza and influenza-like illness (ILI) in a nonelderly population.
  
  
  METHODS
  Patients with medical reimbursement claims for influenza in the 1996-1997 season were identified from the automated database of a large private New England Insurer (NEI). Influenza care during the 21- day follow-up period was characterized according to age, gender, vaccine status, co-morbidities, prior influenza/ILI episodes, treatments, and recent health care costs and related diagnoses.
  
  
  RESULTS
  There were 6,241 patients. Approximately 20% had preexisting chronic lung disease. Overall, 23% had health care services for possible complications, among which respiratory diagnoses were the most common (13%). Two percent of the influenza/ILI episodes involved hospitalization, with a median stay of five days. Factors most strongly predictive of hospitalizations and complications were preexisting malignancy (hospitalizations OR = 3.7 and complications OR = 2.4), chronic heart disease (OR = 3.2 and OR = 1.8), diabetes (OR = 2.2 and OR = 1.7) and recent illnesses that would have counted as complications had they occurred during an influenza/ILI episode (hospitalizations OR = 3.2 and complications OR = 1.5). The same factors affected influenza-related costs and total costs of care as dramatically as they affected complication rates.
  
  
  CONCLUSIONS
  Influenza/ILI-related costs are driven by the characteristics that predict complications of influenza. Patients with chronic illness and those with recent acute respiratory events are the most likely to experience complications and hospitalizations.
authors.affiliation: Ingenix Pharmaceutical Services, Epidemiology Division, Newton Lower Falls, Massachusetts, USA. debra.irwin@ingenixPS.com <debra.irwin@ingenixPS.com>
authors.forename: D E
authors.initials: DE
authors.lastname: Irwin
authors.forename: L B
authors.initials: LB
authors.lastname: Weatherby
authors.forename: W Y
authors.initials: WY
authors.lastname: Huang
authors.forename: D M
authors.initials: DM
authors.lastname: Rosenberg
authors.forename: S F
authors.initials: SF
authors.lastname: Cook
authors.forename: A M
authors.initials: AM
authors.lastname: Walker
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D017281:Cost of Illness; D016527:Drug Costs; D017050:Episode of Care; D005260:Female; D017048:Health Care Costs / Q000145:classification / Q000706:statistics & numerical data*; D006296:Health Services / Q000706:statistics & numerical data; D017721:Hospital Costs; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007251:Influenza, Human / Q000150:complications* / Q000191:economics* / Q000453:epidemiology / Q000628:therapy; D007345:Insurance Claim Review; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009511:New England / Q000453:epidemiology; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012307:Risk Factors