title: Use of attribute association error probability estimates to evaluate quality of medical record geocodes.
abstract: BACKGROUND
  The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends.
  
  
  METHODS
  A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research.
  
  
  RESULTS
  We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations.
  
  
  CONCLUSIONS
  The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics.
authors.affiliation: North Carolina Central Cancer Registry, Raleigh, NC, USA. Christian.klaus@dhhs.nc.gov.
authors.forename: Christian A
authors.initials: CA
authors.lastname: Klaus
authors.affiliation: North Carolina Center for Geographic Information and Analysis, Raleigh, NC, USA. luis.carrasco@nc.gov.
authors.forename: Luis E
authors.initials: LE
authors.lastname: Carrasco
authors.affiliation: Department of Geography, Texas A&M University, College Station, TX, USA. Daniel.goldberg@tamu.edu.|Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA. Daniel.goldberg@tamu.edu.
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Goldberg
authors.affiliation: Department of Geography and Urban Studies, Temple University, Philadelphia, PA, USA. khenry1@temple.edu.
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Henry
authors.affiliation: North American Association of Central Cancer Registries, Springfield, IL, USA. rsherman@naaccr.org.
authors.forename: Recinda L
authors.initials: RL
authors.lastname: Sherman
mesh_terms: D015982:Bias; D000068598:Data Accuracy; D062305:Geographic Mapping; D008498:Medical Record Linkage; D008499:Medical Records / Q000706:statistics & numerical data; D009657:North Carolina; D011336:Probability; D012042:Registries; D012044:Regression Analysis