title: An algorithm to identify preterm infants in administrative claims data.
abstract: OBJECTIVE
  To develop and validate an algorithm to identify preterm infants in the absence of birth certificates within Medicaid data.
  
  
  METHODS
  Medicaid fee-for-service claims data from Florida (FL) and Texas (TX) were linked to vital statistics data for infants who were continuously eligible during the first 3 months following birth or died within that period. Prematurity was defined as less than 34 weeks gestational age. Using FL as exploratory dataset and vital statistics birth data as gold standard, we developed a logistic regression model from diagnostic and procedure codes commonly associated with preterm care, creating a prematurity score for each infant. A score cutoff was selected that maximized sensitivity while maintaining a positive predictive value (PPV) ≥ 90%. Confirmatory analyses were conducted in the TX datasets.
  
  
  RESULTS
  The prevalence of prematurity was 5.2% (95%CI: 5.1-5.2) and 4.5% (95%CI: 4.4-4.6) in FL and TX, respectively. Using only gestational age International Classification of Disease version 9, Clinical Modification (ICD-9-CM) codes (765.20-765.27) associated with inpatient claims achieved sensitivity of 25.7% (FL) and 12.5% (TX), specificity of 99.9% (FL) and (TX), and PPV of 91.7% (FL) and 84.8% (TX). The model had excellent discriminatory validity with a c-statistic of 0.928 (95%CI: 0.925-0.931). The selected cutoff point achieved sensitivity of 52.6%, specificity of 99.8%, and PPV of 91.7% in FL. In TX, sensitivity was 46.8%, specificity was 99.9%, and PPV was 82.2%.
  
  
  CONCLUSIONS
  Identification of prematurity based on gestational age ICD-9-CM codes is not sensitive. The prematurity score has superior construct validity and allows more comprehensive identification of preterm infants in the absence of birth certificates.
authors.affiliation: Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA. efe1odia@ufl.edu
authors.forename: Efe
authors.initials: E
authors.lastname: Eworuke
authors.forename: Christian
authors.initials: C
authors.lastname: Hampp
authors.forename: Arwa
authors.initials: A
authors.lastname: Saidi
authors.forename: Almut G
authors.initials: AG
authors.lastname: Winterstein
mesh_terms: D000465:Algorithms; D005431:Florida / Q000453:epidemiology; D005865:Gestational Age; D006801:Humans; D007226:Infant Mortality / Q000639:trends; D007231:Infant, Newborn; D007234:Infant, Premature; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D038801:International Classification of Diseases; D016015:Logistic Models; D008484:Medicaid; D047928:Premature Birth / Q000145:classification / Q000453:epidemiology* / Q000401:mortality; D015995:Prevalence; D015203:Reproducibility of Results; D013781:Texas / Q000453:epidemiology; D014481:United States