title: A comparison of comorbidities obtained from hospital administrative data and medical charts in older patients with pneumonia.
abstract: BACKGROUND
  The use of comorbidities in risk adjustment for health outcomes research is frequently necessary to explain some of the observed variations. Medical charts reviews to obtain information on comorbidities is laborious. Increasingly, electronic health care databases have provided an alternative for health services researchers to obtain comorbidity information. However, the rates obtained from databases may be either over- or under-reported. This study aims to (a) quantify the agreement between administrative data and medical charts review across a set of comorbidities; and (b) examine the factors associated with under- or over-reporting of comorbidities by administrative data.
  
  
  METHODS
  This is a retrospective cross-sectional study of patients aged 55 years and above, hospitalized for pneumonia at 3 acute care hospitals. Information on comorbidities were obtained from an electronic administrative database and compared with information from medical charts review. Logistic regression was performed to identify factors that were associated with under- or over-reporting of comorbidities by administrative data.
  
  
  RESULTS
  The prevalence of almost all comorbidities obtained from administrative data was lower than that obtained from medical charts review. Agreement between comorbidities obtained from medical charts and administrative data ranged from poor to very strong (kappa 0.01 to 0.78). Factors associated with over-reporting of comorbidities were increased length of hospital stay, disease severity, and death in hospital. In contrast, those associated with under-reporting were number of comorbidities, age, and hospital admission in the previous 90 days.
  
  
  CONCLUSIONS
  The validity of using secondary diagnoses from administrative data as an alternative to medical charts for identification of comorbidities varies with the specific condition in question, and is influenced by factors such as age, number of comorbidities, hospital admission in the previous 90 days, severity of illness, length of hospitalization, and whether inhospital death occurred. These factors need to be taken into account when relying on administrative data for comorbidity information.
authors.affiliation: Health Services and Outcomes Research, National Healthcare Group, 6 Commonwealth Lane, #04-01/02 GMTI Building, Singapore 149547. wai_fung_chong@nhg.com.sg
authors.forename: Wai Fung
authors.initials: WF
authors.lastname: Chong
authors.forename: Yew Yoong
authors.initials: YY
authors.lastname: Ding
authors.forename: Bee Hoon
authors.initials: BH
authors.lastname: Heng
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D003430:Cross-Sectional Studies; D005260:Female; D015577:Geriatric Assessment / Q000706:statistics & numerical data*; D006305:Health Status Indicators; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D011014:Pneumonia / Q000453:epidemiology / Q000473:pathology*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012189:Retrospective Studies; D018570:Risk Assessment; D012846:Singapore / Q000453:epidemiology