{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T02:46:20Z","timestamp":1758595580916,"version":"3.44.0"},"reference-count":31,"publisher":"PAGEPress Publications","issue":"2","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geospat Health"],"abstract":"<jats:p>This study aimed to compare different address geocoding services and their applicability to epidemiological surveillance using dengue as an example. We applied a cross-sectional, descriptive study based on case notifications in the Notifiable Diseases Information System (SINAN) for the Brazilian capital in 2014 that includes complete postal code (CEP) information identified in the National Address Database for Statistical Purposes (CNEFE), which is considered the \u2018gold standard\u2019 for accuracy analysis. For records without CEP, georeferencing was performed through linkage of the original database with four geocoding tools: Google Maps, CNEFE, OpenStreetMap (OSM) and ArcGIS. Variables used for georeferencing were \u2018street name\u2019, \u2018code for municipality\/ city of residency\u2019 and \u2018State\u2019 using accuracy rate estimate and mean spatial error (MSE) of case locations. The two most accurate models were used for kernel density (KD) analysis which is valuable for identifying priority areas for intervention. There were 18,206 dengue cases, 109 (0.6%) of which had correct CEP information and geocoded using CNEFE bases. The linkage results showed that Google Maps application programming interface (API) had an accuracy of 17.6% (MSE: 178.89km), CNEFE 9.0% (MSE: 17.24km), OSM 7.1% (MSE: 564.19km), and ArcGIS 3.7% (MSE: 2001.33km). Although overall accuracy values were modest, the best two models proven to be effective for KD analysis revealed similar patterns between Google Maps and CNEFE results but choosing the preferable geocoding technique should also financial resources. This study recommends the use of Google Maps API for georeferencing, followed by CNEFE.<\/jats:p>","DOI":"10.4081\/gh.2025.1403","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T08:14:42Z","timestamp":1756800882000},"source":"Crossref","is-referenced-by-count":0,"title":["Use of geocoding techniques for epidemiological surveillance in the Federal District, Brazil: a case study using dengue"],"prefix":"10.4081","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8957-450X","authenticated-orcid":false,"given":"Lucas","family":"Sanglard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2268-8742","authenticated-orcid":false,"given":"Klauss K. S.","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5085-5670","authenticated-orcid":false,"given":"Walter Massa","family":"Ramalho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2549","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"27366","doi-asserted-by":"publisher","DOI":"10.5123\/S1679-49742008000100006"},{"key":"27367","doi-asserted-by":"publisher","DOI":"10.1590\/1413-812320172210.10852017"},{"key":"27368","doi-asserted-by":"publisher","DOI":"10.21105\/joss.03544"},{"key":"27369","unstructured":"CODEPLAN. 2020. Atlas do Distrito Federal \u2013 Cap\u00edtulo 5: Meio Socioecon\u00f4mico. Companhia de Planejamento do Distrito Federal. Marina Artes Gr\u00e1ficas e Editora: 74:93."},{"key":"27370","doi-asserted-by":"publisher","DOI":"10.11606\/9786586810233"},{"key":"27371","doi-asserted-by":"publisher","DOI":"10.1590\/s2237-96222022000300004"},{"key":"27372","doi-asserted-by":"publisher","DOI":"10.3390\/tropicalmed8120519"},{"key":"27373","unstructured":"Google, 2025. Geocoding API: Overview. Available from: https:\/\/developers.google.com\/maps\/documentation\/geocoding\/overview?hl=pt-br."},{"key":"27374","unstructured":"IBGE, 2025. Available from: https:\/\/www.ibge.gov.br\/estatisticas\/sociais\/populacao\/38734-cadastro-nacional-de-enderecos-para-fins-estatisticos.html."},{"key":"27375","doi-asserted-by":"publisher","DOI":"10.2307\/2289924"},{"key":"27376","doi-asserted-by":"publisher","DOI":"10.2196\/46810"},{"key":"27377","doi-asserted-by":"publisher","DOI":"10.1186\/s12942-023-00347-2"},{"key":"27378","unstructured":"Lustosa JLS, 2017. Geocoding dengue cases for spatial analisys. Conference Paper. Available from: https:\/\/www.researchgate.net\/publication\/320372257_Geocoding_Dengue_Cases_for_Spatial_Analysis"},{"key":"27379","doi-asserted-by":"publisher","DOI":"10.1590\/1414-462X201400020013"},{"key":"27380","doi-asserted-by":"publisher","DOI":"10.4081\/gh.2017.526"},{"key":"27381","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2012.1199"},{"key":"27382","unstructured":"MoH, 2024. Minist\u00e9rio da Sa\u00fade Brasil. Secretaria de Vigil\u00e2ncia em Sa\u00fade e Ambiente. Departamento de A\u00e7\u00f5es Estrat\u00e9gicas de Epidemiologia e Vigil\u00e2ncia em Sa\u00fade e Ambiente. Guia de vigil\u00e2ncia em sa\u00fade: volume 1. 6th ed. rev. Bras\u00edlia: Minist\u00e9rio da Sa\u00fade. 3 v. 465 pp."},{"key":"27383","unstructured":"MoH, 2025a. Minist\u00e9rio da Sa\u00fade Brasil. Painel de Monitoramento de Arboviroses. Bras\u00edlia: Minist\u00e9rio da Sa\u00fade. Available from: https:\/\/www.gov.br\/saude\/pt-br\/assuntos\/saude-de-a-a-z\/a\/aedes-aegypti\/monitoramento-das-arboviroses."},{"key":"27384","unstructured":"MoH, 2025b. Minist\u00e9rio da Sa\u00fade Brasil,. Secretaria de Vigil\u00e2ncia em Sa\u00fade e Ambiente. Departamento de Doen\u00e7as Transmiss\u00edveis. Plano de conting\u00eancia nacional para dengue, chikungunya e Zika. Bras\u00edlia: Minist\u00e9rio da Sa\u00fade. 102 pp."},{"key":"27385","doi-asserted-by":"publisher","DOI":"10.1590\/0001-3765202020200700"},{"key":"27386","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4970881"},{"key":"27387","doi-asserted-by":"publisher","DOI":"10.20344\/amp.20569"},{"key":"27388","unstructured":"SES-DF. 2024. Secretaria de Sa\u00fade do Distrito Federal. Monitoramento dos casos de dengue, febre de chikungunya, doen\u00e7a aguda pelo v\u00edrus zika e febre amarela at\u00e9 a Semana Epidemiol\u00f3gica 52 de 2024, no Distrito Federal. Bras\u00edlia, 1-16."},{"key":"27389","doi-asserted-by":"publisher","DOI":"10.5123\/S1679-49742017000400018"},{"key":"27390","unstructured":"Skaba DA, 2009. Metodologias de geocodifica\u00e7\u00e3o dos dados da sa\u00fade. Rio de Janeiro: Escola Nacional de Sa\u00fade P\u00fablica Sergio Arouca, Funda\u00e7\u00e3o Oswaldo Cruz. [Thesis] 155 pp."},{"key":"27391","doi-asserted-by":"publisher","DOI":"10.1590\/S0102-311X2004000600037"},{"key":"27392","unstructured":"Vieira FS, 2020. Health financing in Brazil and the goals of the 2030 Agenda: high risk of failure. Rev Saude Publica 54:127."},{"key":"27393","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01686"},{"key":"27394","unstructured":"Wickham H. 2021. Mastering Shiny. 1st ed. Sebastopol. O'Reilly Media, Incorporated; 400 pp."},{"key":"27395","unstructured":"Winkler WE, 1990. String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey Research Methods. American Statistical Association; 8."},{"key":"27396","unstructured":"World Health Organization, 2025. Dengue and severe dengue. Geneva: WHO. 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