title: Tools for address georeferencing - limitations and opportunities every public health professional should be aware of.
abstract: Various address georeferencing (AG) tools are currently available. But little is known about the quality of each tool. Using data from the EPIPorto cohort we compared the most commonly used AG tools in terms of positional error (PE) and subjects' misclassification according to census tract socioeconomic status (SES), a widely used variable in epidemiologic studies. Participants of the EPIPorto cohort (n = 2427) were georeferenced using Geographical Information Systems (GIS) and Google Earth (GE). One hundred were randomly selected and georeferenced using three additional tools: 1) cadastral maps (gold-standard); 2) Global Positioning Systems (GPS) and 3) Google Earth, single and in a batch. Mean PE and the proportion of misclassified individuals were compared. Google Earth showed lower PE than GIS, but 10% of the addresses were imprecisely positioned. Thirty-eight, 27, 16 and 14% of the participants were located in the wrong census tract by GIS, GPS, GE (batch) and GE (single), respectively (p<0.001). Misclassification according to SES was less frequent but still non-negligible -14.4, 8.1, 4.2 and 2% (p<0.001). The quality of georeferencing differed substantially between AG tools. GE seems to be the best tool, but only if prudently used. Epidemiologic studies using spatial data should start including information on the quality and accuracy of their georeferencing tools and spatial datasets.
authors.affiliation: Instituto de Engenharia Biomédica - INEB, Universidade do Porto, Porto, Portugal; Departamento de Epidemiologia Clínica, Medicina Preditiva e Saúde Pública, Faculdade de Medicina do Porto, Universidade do Porto, Porto, Portugal; Instituto de Saúde Pública da Universidade do Porto - ISPUP, Porto, Portugal.
authors.forename: Ana Isabel
authors.initials: AI
authors.lastname: Ribeiro
authors.affiliation: Instituto de Engenharia Biomédica - INEB, Universidade do Porto, Porto, Portugal; Instituto de Saúde Pública da Universidade do Porto - ISPUP, Porto, Portugal.
authors.forename: Andreia
authors.initials: A
authors.lastname: Olhero
authors.affiliation: Instituto de Engenharia Biomédica - INEB, Universidade do Porto, Porto, Portugal; Instituto de Saúde Pública da Universidade do Porto - ISPUP, Porto, Portugal.
authors.forename: Hugo
authors.initials: H
authors.lastname: Teixeira
authors.affiliation: Instituto de Engenharia Biomédica - INEB, Universidade do Porto, Porto, Portugal; Departamento de Epidemiologia Clínica, Medicina Preditiva e Saúde Pública, Faculdade de Medicina do Porto, Universidade do Porto, Porto, Portugal; Instituto de Saúde Pública da Universidade do Porto - ISPUP, Porto, Portugal.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Magalhães
authors.affiliation: Instituto de Engenharia Biomédica - INEB, Universidade do Porto, Porto, Portugal; Departamento de Epidemiologia Clínica, Medicina Preditiva e Saúde Pública, Faculdade de Medicina do Porto, Universidade do Porto, Porto, Portugal; Instituto de Saúde Pública da Universidade do Porto - ISPUP, Porto, Portugal.
authors.forename: Maria Fátima
authors.initials: MF
authors.lastname: Pina
mesh_terms: D000328:Adult; D000368:Aged; D016208:Databases, Factual; D040362:Geographic Information Systems; D062305:Geographic Mapping; D006801:Humans; D008875:Middle Aged; D009786:Occupational Health Services; D012923:Social Class; D055815:Young Adult