title: Application of a national administrative case definition for the identification of pre-existing diabetes mellitus in pregnancy.
abstract: BACKGROUND
  Accurate ascertainment of pregnant women with pre-existing diabetes allows for the comprehensive surveillance of maternal and neonatal outcomes associated with this chronic disease.
  
  
  METHODS
  To determine the accuracy of case definitions for pre-existing diabetes mellitus when applied to a pregnant population, a cohort of women who were pregnant in Nova Scotia, Canada, between 1991 and 2003 was obtained from a population-based provincial perinatal database, the Nova Scotia Atlee Perinatal Database (NSAPD). Person-level data from administrative databases using hospital discharge abstract data and outpatient physician services data were linked to this cohort. Various algorithms for defining diabetes mellitus from the administrative data, including the algorithm suggested by the National Diabetes Surveillance System (NDSS), were compared to a reference standard definition from the NSAPD.
  
  
  RESULTS
  Validation of the NDSS case definition applied to this pregnant population demonstrated a sensitivity of 87% and a positive predictive value (PPV) of 66.4%. Use of ICD-9 and ICD-10 diagnostic codes among hospitalizations with diabetes mellitus in pregnancy showed important increases in sensitivity and PPV, especially for those pregnancies delivered in tertiary centres. In this population, pregnancy-related administrative data from the hospitalization database alone appear to be a more accurate data source for identifying pre-existing diabetes than applying the NDSS case definition, particularly when pregnant women are delivered in a tertiary hospital.
  
  
  CONCLUSIONS
  Although the NDSS definition of diabetes performs reasonably well compared to a reference standard definition of diabetes, using this definition for evaluating maternal and perinatal outcomes associated with diabetes in pregnancy will result in a certain degree of misclassification and, therefore, biased estimates of outcomes.
authors: Department of Obstetrics and Gynaecology, Dalhousie University, Halifax, Nova Scotia, Canada. victoria.allen@dal.ca
authors: V M
authors: VM
authors: Allen
authors: L
authors: L
authors: Dodds
authors: A
authors: A
authors: Spencer
authors: E A
authors: EA
authors: Cummings
authors: N
authors: N
authors: MacDonald
authors: G
authors: G
authors: Kephart
mesh_terms: D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000453:epidemiology*; D005260:Female; D006801:Humans; D015994:Incidence; D038801:International Classification of Diseases; D009674:Nova Scotia / Q000453:epidemiology; D011237:Predictive Value of Tests; D011247:Pregnancy; D011254:Pregnancy in Diabetics / Q000453:epidemiology*; D015995:Prevalence