title: Evaluating the use of existing data sources, probabilistic linkage, and multiple imputation to build population-based injury databases across phases of trauma care.
abstract: OBJECTIVE
  The objective was to evaluate the process of using existing data sources, probabilistic linkage, and multiple imputation to create large population-based injury databases matched to outcomes.
  
  
  METHODS
  This was a retrospective cohort study of injured children and adults transported by 94 emergency medical systems (EMS) agencies to 122 hospitals in seven regions of the western United States over a 36-month period (2006 to 2008). All injured patients evaluated by EMS personnel within specific geographic catchment areas were included, regardless of field disposition or outcome. The authors performed probabilistic linkage of EMS records to four hospital and postdischarge data sources (emergency department [ED] data, patient discharge data, trauma registries, and vital statistics files) and then handled missing values using multiple imputation. The authors compare and evaluate matched records, match rates (proportion of matches among eligible patients), and injury outcomes within and across sites.
  
  
  RESULTS
  There were 381,719 injured patients evaluated by EMS personnel in the seven regions. Among transported patients, match rates ranged from 14.9% to 87.5% and were directly affected by the availability of hospital data sources and proportion of missing values for key linkage variables. For vital statistics records (1-year mortality), estimated match rates ranged from 88.0% to 98.7%. Use of multiple imputation (compared to complete case analysis) reduced bias for injury outcomes, although sample size, percentage missing, type of variable, and combined-site versus single-site imputation models all affected the resulting estimates and variance.
  
  
  CONCLUSIONS
  This project demonstrates the feasibility and describes the process of constructing population-based injury databases across multiple phases of care using existing data sources and commonly available analytic methods. Attention to key linkage variables and decisions for handling missing values can be used to increase match rates between data sources, minimize bias, and preserve sampling design.
authors.affiliation: Center for Policy and Research in Emergency Medicine, Department of Emergency Medicine, Oregon Health & Science University, Portland, USA. newgardc@ohsu.edu
authors.forename: Craig
authors.initials: C
authors.lastname: Newgard
authors.forename: Susan
authors.initials: S
authors.lastname: Malveau
authors.forename: Kristan
authors.initials: K
authors.lastname: Staudenmayer
authors.forename: N Ewen
authors.initials: NE
authors.lastname: Wang
authors.forename: Renee Y
authors.initials: RY
authors.lastname: Hsia
authors.forename: N Clay
authors.initials: NC
authors.lastname: Mann
authors.forename: James F
authors.initials: JF
authors.lastname: Holmes
authors.forename: Nathan
authors.initials: N
authors.lastname: Kuppermann
authors.forename: Jason S
authors.initials: JS
authors.lastname: Haukoos
authors.forename: Eileen M
authors.initials: EM
authors.lastname: Bulger
authors.forename: Mengtao
authors.initials: M
authors.lastname: Dai
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Cook
mesh_terms: D000328:Adult; D002648:Child; D016208:Databases, Factual; D004632:Emergency Medical Services; D005260:Female; D006801:Humans; D008297:Male; D008498:Medical Record Linkage; D015233:Models, Statistical; D010351:Patient Discharge / Q000706:statistics & numerical data; D011336:Probability; D012042:Registries; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D014798:Vital Statistics; D014947:Wounds and Injuries / Q000453:epidemiology / Q000628:therapy*