title: Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification analysis.
abstract: BACKGROUND
  The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways.
  
  
  METHODS
  We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups.
  
  
  RESULTS
  Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups.
  
  
  CONCLUSIONS
  These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.
authors.affiliation: Boston University School of Public Health, Boston, MA, USA. KLane@BU.edu.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Lane
authors.forename: Madeleine
authors.initials: M
authors.lastname: Kangsen Scammell
authors.forename: Jonathan I
authors.initials: JI
authors.lastname: Levy
authors.forename: Christina H
authors.initials: CH
authors.lastname: Fuller
authors.forename: Ron
authors.initials: R
authors.lastname: Parambi
authors.forename: Wig
authors.initials: W
authors.lastname: Zamore
authors.forename: Mkaya
authors.initials: M
authors.lastname: Mwamburi
authors.forename: Doug
authors.initials: D
authors.lastname: Brugge
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D001900:Boston; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000379:methods*; D005260:Female; D040362:Geographic Information Systems / Q000592:standards*; D062305:Geographic Mapping; D006801:Humans; D008297:Male; D012107:Research Design / Q000592:standards*; D012111:Residence Characteristics
chemical_list: D000393:Air Pollutants