On the effect of diagnostic misclassification bias on the observed spatial pattern in regional count data - A case study using West Nile virus mortality data from Ontario, 2005

• Type of Material: Article
• Authors: Olaf Berke, University of Guelph; Lance Waller, Emory University
• Language: English
• Date: 2010-07-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2010 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.sste.2010.03.004
• Title of Journal or Parent Work: Spatial and Spatio-temporal Epidemiology
• ISSN: 1877-5845
• Volume: 1
• Issue: 2-3
• Start Page: 117
• End Page: 122
• Grant/Funding Information: The research by the first author was supported by Grant 200017 from the Ontario Ministry of Agriculture, Food and Rural Affairs.
• Keywords: Incidence; Humans; Animals; Diagnostic Errors; West Nile Fever; Cluster Analysis; Female; Disease Outbreaks; Ontario; Models, Statistical; West Nile virus; Birds; Spatial Analysis; Male; Bias; Survival Analysis
• Research Categories: Health Sciences, Public Health; Biology, Biostatistics; Health Sciences, Medicine and Surgery

Abstract

Geographic epidemiology is concerned with the investigation of spatially referenced data to discover spatial patterns in the health status of populations. In this context it is generally assumed that a perfect diagnostic test is used to classify individuals as being positive or negative, meaning the health status is measured without error. In this work the effect of an imperfect diagnostic test on spatial patterns of disease in regional count data is investigated in a case study. Specifically the misclassification effect on the semivariogram, Moran's I statistic and the spatial scan test are evaluated for the situation of West Nile virus infections among dead birds sampled from the 30 public health units of southern Ontario in 2005. We illustrate that under large sample conditions no serious spatial bias is introduced by use of an imperfect diagnostic test as long as the imperfection itself is spatially unbiased.