title: Geographic Information Systems to Assess External Validity in Randomized Trials.
abstract: BACKGROUND
  To support claims that RCTs can reduce health disparities (i.e., are translational), it is imperative that methodologies exist to evaluate the tenability of external validity in RCTs when probabilistic sampling of participants is not employed. Typically, attempts at establishing post hoc external validity are limited to a few comparisons across convenience variables, which must be available in both sample and population. A Type 2 diabetes RCT was used as an example of a method that uses a geographic information system to assess external validity in the absence of a priori probabilistic community-wide diabetes risk sampling strategy.
  
  
  METHODS
  A geographic information system, 2009-2013 county death certificate records, and 2013-2014 electronic medical records were used to identify community-wide diabetes prevalence. Color-coded diabetes density maps provided visual representation of these densities. Chi-square goodness of fit statistic/analysis tested the degree to which distribution of RCT participants varied across density classes compared to what would be expected, given simple random sampling of the county population. Analyses were conducted in 2016.
  
  
  RESULTS
  Diabetes prevalence areas as represented by death certificate and electronic medical records were distributed similarly. The simple random sample model was not a good fit for death certificate record (chi-square, 17.63; p=0.0001) and electronic medical record data (chi-square, 28.92; p<0.0001). Generally, RCT participants were oversampled in high-diabetes density areas.
  
  
  CONCLUSIONS
  Location is a highly reliable "principal variable" associated with health disparities. It serves as a directly measurable proxy for high-risk underserved communities, thus offering an effective and practical approach for examining external validity of RCTs.
authors.affiliation: Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina. Electronic address: msavoca@wakehealth.edu.
authors.forename: Margaret R
authors.initials: MR
authors.lastname: Savoca
authors.affiliation: Division of Pediatric Clinical Research, Department of Pediatrics, and Division of Biostatistics, Public Health Sciences, University of Miami Leonard M. Miller School of Medicine, Miami, Florida.
authors.forename: David A
authors.initials: DA
authors.lastname: Ludwig
authors.affiliation: Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
authors.forename: Stedman T
authors.initials: ST
authors.lastname: Jones
authors.affiliation: MapForsyth|City-County Geographic Information Office, Winston-Salem, North Carolina.
authors.forename: K
authors.initials: K
authors.lastname: Jason Clodfelter
authors.affiliation: MapForsyth|City-County Geographic Information Office, Winston-Salem, North Carolina.
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Sloop
authors.affiliation: Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
authors.forename: Linda Y
authors.initials: LY
authors.lastname: Bollhalter
authors.affiliation: Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina; Maya Angelou Center for Health Equity, Wake Forest School of Medicine, Winston-Salem, North Carolina.
authors.forename: Alain G
authors.initials: AG
authors.lastname: Bertoni
mesh_terms: D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology* / Q000628:therapy; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005069:Evaluation Studies as Topic; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D054625:Healthcare Disparities / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors